Class Details

Price: $1,500

Course Includes:

  • Instructor-led training and exercises
  • Take-home code templates
  • Course reference guides and cheat sheets for additional support at home
  • Case studies and interactive practice scenarios to help develop probem-solving skills
  • Courseware books, notepads, pens, highlighters and other materials
  • Full breakfast with variety of bagels, fruits, yogurt, doughnuts and juice
  • Tea, coffee, and soda available all day
  • Freshly baked cookies every afternoon - * only at participating locations

This 4-day course teaches analysts how to predict behaviors, events and make product recommendations based on multiple factors. 




Course Outline

Module 1: Introduction to Classification and Supervised Machine Learniing

  • Lesson 1: Commercial applications of classification models and predictive analytics

Module 2: Classification Algorithms

  • Lesson 1: k-Nearest Neighbors
  • Lesson 2: Association rules
  • Lesson 3: Decision trees: gini coefficient and information gain
  • Lesson 4: Random forests

Module 3: Fine Tuning Your Model

  • Lesson 1: Confusion matrices and misclassification rates
  • Lesson 2: Base line errors
  • Lesson 3: ROC curves
  • Lesson 4: AUC values
  • Lesson 5: Bagging
  • Lesson 6: Boosting

Module 4: Advanced Classification Algorithms

  • Lesson 1: Support vector machines
  • Lesson 2: Logistic regression
  • Lesson 3: Multivariate logistic regression
  • Lesson 4: Penalized logistic regression (lasso, ridge, elastic net)
  • Lesson 5: Naive Bayes
  • Lesson 6: Linear discrimination analysis
  • Lesson 7: Additional tips and resources

Objectives

At the conclusion of this course, participants will be able to do the following:

  • Build classification models
  • Evaluate the accuracy of predictive algorithms