Predictive 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.

This 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.

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 to test and validating samples into Machine Learning models
• Submit an overview of the best analytics solutions
• Implement fine-tuned estimation with complete predictive models 

This 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 screenshot applications on comparative technologies such as SPSS, SAS, Statistica, and Excel.

Day One - Data Analysis and Simple Regression

• Introduction to Data Analysis Logic
• Testing two groups on their means and proportions
• Profiling two groups in one single chart
• Testing multiple groups on their means and proportions
• Profiling multiple groups in one single chart
• Simple regression
• Regression vs. Correlation
• Sensitivity analysis of quantitative variables

Day Two - Multiple and Logistic Regressions

• Introduction to Machine Learning
• The Gradient Descent logic
• Multiple Regression vs. Simple Regression
• Variability analysis for estimations
• Dummy variables
• Similarities and differences between Logistic and Multiple regressions
• Simplifying complex models
• Stepwise regression

Day Three - Discriminant Analysis

• Optimized Profiling
• Two-Group Discriminant Function
• Attribution of Cases
• Model Evaluation
• Classification Functions
• Mahalanobis Squared Distances
• Probability Method
• Model’s Reduction
• Generalized Discriminant Analysis

Day Four - Decision Trees

• What are Decision Trees?
• Binary Trees
• Quality of a decision tree
• Rules of pruning
• CART: Classification Tree
• CART: Regression Tree
• CHAID tree
• Random Forest tree

Day Five - Nearest Neighbor, Bayesian, Neural Network and Deep Learning

• Conditional probabilities
• Prediction by probabilities
• Distance from neighbours
• K nearest distances from neighbours
• Weights in a Neural Network model
• Hidden layers role
• Neural Network pros and cons
• Deep Learning
• Introduction to Big Data