Machine Learning and Predictive Models - Virtual Learning

Course Objectives

Machine Learning and Predictive Models - Virtual Learning

Course Objectives
By the end of the course, participants will be able to:

Understand the true meaning of Machine Learning
Comprehend the key differences between Data Analysis and Machine Learning
Apply testing and validating samples into Machine Learning models
Submit an overview of the best analytic solutions
Implement fine tuned estimation with complete predictive models 
Target Audience
Any 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.

Target Competencies
Predictive Analysis
Predictive Models
Data Analysis
Data Analytic Models

Course Outline

Data Analysis and Simple RegressionIntroduction to Data Analysis LogicTesting two groups on their means and proportionsProfiling two groups in one single chartTesting multiple groups on their means and proportionsProfiling multiple groups in one single chartSimple regressionRegression vs. CorrelationSensitivity analysis of quantitative variables
Multiple and Logistic RegressionsIntroduction to Machine LearningThe Gradient Descent logicMultiple Regression vs. Simple RegressionVariability analysis for estimationsDummy variablesSimilarities and differences between Logistic and Multiple regressionsSimplifying complex modelsStepwise regression
Discriminant AnalysisOptimized ProfilingTwo-Group Discriminant FunctionAttribution of CasesModel EvaluationClassification FunctionsMahalanobis Squared DistancesProbability MethodModel’s ReductionGeneralized Discriminant Analysis
Decision TreesWhat are Decision Trees?Binary TreesQuality of a decision treeRules of pruningCART: Classification TreeCART: Regression TreeCHAID treeRandom Forest tree
Nearest Neighbor, Bayesian, Neural Network and Deep LearningConditional probabilitiesPrediction by probabilitiesDistance from neighborsK nearest distances from neighborsWeights in a Neural Network modelHidden layers roleNeural Network pros and consDeep LearningIntroduction to Big Data

Per participant


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