Course Objectives

• This Course Objective Hasn't Been Provided Yet

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.

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

This course hasn't been scheduled yet, however you can request for In-House training