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DTSTART;VALUE=DATE:20260911
DTEND;VALUE=DATE:20260916
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061727-1789084800-1789516799@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.		\n			organization Line Course\n			\n		\n		Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2026-09-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260911
DTEND;VALUE=DATE:20260923
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058808-1789084800-1790121599@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *Address		\n			Type Email Line\n			\n		\n		Address Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2026-09-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261011
DTEND;VALUE=DATE:20261016
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061728-1791676800-1792108799@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)		\n			2 Applicant Number\n			\n		\n		Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2026-10-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261011
DTEND;VALUE=DATE:20261023
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058809-1791676800-1792713599@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.		\n			Line Email Applicant\n			\n		\n		Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2026-10-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261111
DTEND;VALUE=DATE:20261116
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061729-1794355200-1794787199@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm Email		\n			of Phone organization\n			\n		\n		Phone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2026-11-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261111
DTEND;VALUE=DATE:20261123
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10035629-1794355200-1795391999@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.		\n			from 2 Email\n			\n		\n		Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2026-11-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261211
DTEND;VALUE=DATE:20261216
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061730-1796947200-1797379199@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Applicant Additional Information\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2026-12-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261211
DTEND;VALUE=DATE:20261223
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058810-1796947200-1797983999@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.		\n			Number Email details\n			\n		\n		Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2026-12-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270111
DTEND;VALUE=DATE:20270116
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061731-1799625600-1800057599@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Phone Name Additional\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-01-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270111
DTEND;VALUE=DATE:20270123
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058811-1799625600-1800662399@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm Email		\n			Phone Details organization\n			\n		\n		Phone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-01-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270211
DTEND;VALUE=DATE:20270216
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061732-1802304000-1802735999@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Applicant Line 3\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-02-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270211
DTEND;VALUE=DATE:20270223
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058812-1802304000-1803340799@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)		\n			Details organization Details\n			\n		\n		Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-02-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270311
DTEND;VALUE=DATE:20270316
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061733-1804723200-1805155199@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)		\n			Contact Organization 2\n			\n		\n		Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-03-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270311
DTEND;VALUE=DATE:20270323
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058813-1804723200-1805759999@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLast		\n			Course Details organization\n			\n		\n		Address Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-03-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270411
DTEND;VALUE=DATE:20270416
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061734-1807401600-1807833599@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubai		\n			Number Email Details\n			\n		\n		Additional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-04-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270411
DTEND;VALUE=DATE:20270423
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058814-1807401600-1808438399@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *		\n			from Line Details\n			\n		\n		AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-04-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270511
DTEND;VALUE=DATE:20270516
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061735-1809993600-1810425599@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *		\n			Course Venue 3\n			\n		\n		Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-05-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270511
DTEND;VALUE=DATE:20270523
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058815-1809993600-1811030399@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLast		\n			Details 2 Additional\n			\n		\n		Address Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-05-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270611
DTEND;VALUE=DATE:20270616
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061736-1812672000-1813103999@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm Email		\n			Organization Organization Name\n			\n		\n		Phone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-06-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270611
DTEND;VALUE=DATE:20270623
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058816-1812672000-1813708799@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional Information		\n			Organization Course Details\n			\n		\n		Organization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-06-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270711
DTEND;VALUE=DATE:20270716
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061737-1815264000-1815695999@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLast		\n			Number Course Details\n			\n		\n		Address Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-07-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270711
DTEND;VALUE=DATE:20270723
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058817-1815264000-1816300799@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm Email		\n			Additional Address 3\n			\n		\n		Phone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-07-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270811
DTEND;VALUE=DATE:20270816
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061738-1817942400-1818374399@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Venue Email 2\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-08-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270811
DTEND;VALUE=DATE:20270823
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10035638-1817942400-1818979199@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Information Participants Type\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-08-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270911
DTEND;VALUE=DATE:20270916
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061739-1820620800-1821052799@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubai		\n			3 Additional Number\n			\n		\n		Additional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-09-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20270911
DTEND;VALUE=DATE:20270923
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058818-1820620800-1821657599@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Line Line Address\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-09-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20271011
DTEND;VALUE=DATE:20271016
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061740-1823212800-1823644799@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Additional Organization Name\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-10-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20271011
DTEND;VALUE=DATE:20271023
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058819-1823212800-1824249599@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional Information		\n			Organization Address Course\n			\n		\n		Organization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-10-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20271111
DTEND;VALUE=DATE:20271116
DTSTAMP:20260204T072519Z
CREATED:20250115T220000Z
LAST-MODIFIED:20260204T072519Z
UID:10061741-1825891200-1826323199@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\n\nMultiple and Logistic Regressions\n\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\n\nDiscriminant Analysis\n\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\n\nDecision Trees\n\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLast		\n			Information of Course\n			\n		\n		Email *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLastNumber of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2/2027-11-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/png:https://basatraining.com/wp-content/uploads/2025/01/Machine-Learning.png
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20271111
DTEND;VALUE=DATE:20271123
DTSTAMP:20260203T093150Z
CREATED:20250821T094218Z
LAST-MODIFIED:20260203T093150Z
UID:10058820-1825891200-1826927999@basatraining.com
SUMMARY:Machine Learning and Predictive Models
DESCRIPTION:Machine Learning and Predictive Models\nWhy Attend\nPredictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms\, and their role in the enhancement of predictions in most industries and by most organizations. \nThis course covers all models utilized under different technologies (SAS\, Statistica and SPSS)\, enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations. \n\nCourse Methodology\nThis course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS\, SAS\, Statistica and Excel. \n\n\nCourse Objectives\nBy the end of the course\, participants will be able to: \n\nUnderstand the true meaning of Machine Learning\nComprehend the key differences between Data Analysis and Machine Learning\nApply testing and validating samples into Machine Learning models\nSubmit an overview of the best analytic solutions\nImplement fine-tuned estimation with complete predictive models\n\n\n\nTarget Audience\nAny level of professional interested in how Machine Learning can assist their organization\, would benefit from this course.  These include professionals from industries including\, but not limited to\, banking\, insurance\, retail\, government\, manufacturing\, healthcare\, telecom\, and airlines. \n\n\nTarget Competencies\n\nPredictive Analysis\nPredictive Models\nData Analysis\nData Analytic Models\n\nOutline\n\n\nData Analysis and Simple Regression\n\n\nIntroduction to Data Analysis Logic\nTesting two groups on their means and proportions\nProfiling two groups in one single chart\nTesting multiple groups on their means and proportions\nProfiling multiple groups in one single chart\nSimple regression\nRegression vs. Correlation\nSensitivity analysis of quantitative variables\n\n\nMultiple and Logistic Regressions\n\n\nIntroduction to Machine Learning\nThe Gradient Descent logic\nMultiple Regression vs. Simple Regression\nVariability analysis for estimations\nDummy variables\nSimilarities and differences between Logistic and Multiple regressions\nSimplifying complex models\nStepwise regression\n\n\nDiscriminant Analysis\n\n\nOptimized Profiling\nTwo-Group Discriminant Function\nAttribution of Cases\nModel Evaluation\nClassification Functions\nMahalanobis Squared Distances\nProbability Method\nModel’s Reduction\nGeneralized Discriminant Analysis\n\n\n\n\n\n\nDecision Trees\n\n\nWhat are Decision Trees?\nBinary Trees\nQuality of a Decision Tree\nRules of pruning\nCART: Classification Tree\nCART: Regression Tree\nCHAID Tree\nRandom Forest Tree\n\n\nNearest Neighbor\, Bayesian\, Neural Network and Deep Learning\n\n\nConditional probabilities\nPrediction by probabilities\nDistance from neighbors\nK nearest distances from neighbors\nWeights in a Neural Network model\nHidden layers role\nNeural Network pros and cons\nDeep Learning\nIntroduction to Big Data\n\n\nEvent Coordinated by BASA Training \nYou can also visit our Online Courses Website Excel Elearning \nYou can also get an affordable ebook on ebooksnest.com \n\n\n\n\n\nRegistration Form\nPlease enable JavaScript in your browser to complete this form.Please enable JavaScript in your browser to complete this form.Applicant Details *FirstLastEmail *EmailConfirm EmailPhone Number *Course Type *--- Select Choice ---CertificationsAccounting and FinanceAdministrationAuditing and GovernanceBankingBusiness Continuity and Crisis ManagementCommunication and Writing SkillsConferenceCustomer ServiceData Analytics and Artificial IntelligenceData Management and Business IntelligenceDigital Innovation and TransformationGovernment and Public SectorHuman Resources and TrainingInsurance and Financial ServicesInterpersonal Skills and Self DevelopmentIT Management and Cyber SecurityLeadership and ManagementManagementPlanning and Strategy ManagementProject ManagementPublic RelationsQuality and ProductivityRisk and ComplianceSafety and EnvironmentSecurity ManagementSelect the correct course category (typically found below in the course details)Course Name *Course VenuePretoria\, South AfricaDurban\, South AfricaCape Town\, South AfricaKigali\, RwandaHarare\, ZimbabweDubaiAdditional InformationOrganization Name *AddressAddress Line 2 *FirstLastAddress Line 3 *FirstLastOrganization Contact DetailsFirstLast		\n			Phone Additional Line\n			\n		\n		Number of Participants from organization\n\n\n\n	Participants: 1\nUse the slider to chose whether you want to register only 1 or 10 participantsAdditional Participants detailsSubmit
URL:https://basatraining.com/course/machine-learning-and-predictive-models-3-2-2/2027-11-11/
LOCATION:Pretoria\, 484 Hilda St\, Hatfield\, Pretoria\, Gauteng\, 0083\, South Africa
CATEGORIES:Data Management and Business Intelligence,Data Management and Business Intelligence|Digital Innovation and Transformation
ATTACH;FMTTYPE=image/jpeg:https://basatraining.com/wp-content/uploads/2025/01/growtika-f0JGorLOkw0-unsplash.jpg
ORGANIZER;CN="BASA":MAILTO:info@basatraining.com
END:VEVENT
END:VCALENDAR