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Phoenix TS

Supervised Machine Learning: Classification Algorithms Training

This training teaches analysts how to predict behaviors, events and make product recommendations based on multiple factors.

Course Overview

This training course teaches analysts how to predict behaviors, events and make product recommendations based on multiple factors. By the end of the course, students will be able to build classification models and evaluate the accuracy of predictive algorithms. Classification algorithms answer the questions “What is the person or object like?” “What is the likelihood that an event will happen or that a person is part of a particular group?” Applications include fraud detection, cyber attack and intrusion detection, anticipating employee and customer behavior and detecting other threats and events.

Schedule

Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757.

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Course Outline

Introduction to Classification and Supervised Machine Learning

  • Commercial applications of classification models and predictive analytics

Classification Algorithms

  • k-Nearest Neighbors
  • Association rules
  • Decision trees: gini coefficient and information gain
  • Random forests

Fine Tuning Your Model

  • Confusion matrices and misclassification rates
  • Base line errors
  • ROC curves
  • AUC values
  • Bagging
  • Boosting

Advanced Classification Algorithms

  • Support vector machines
  • Logistic regression
  • Multivariate logistic regression
  • Penalized logistic regression (lasso, ridge, elastic net)
  • Naive Bayes
  • Linear discrimination analysis
  • Additional tips and resources

Supervised Machine Learning: Classification Algorithms Training FAQs

Who should take this course?

This course is intended for leaders and executives who have a good working knowledge of R, have a good background in basic statistics,
need to build predictive models to anticipate market demand, customer behaviors, fraud and security threats; want to stand out as data scientists with advanced predictive modeling skills.

What is the recommended experience for this course?

Students should have taken the Introduction to Data Science, R, and Visualization course or should have the equivalent knowledge of data manipulation, cleaning and visualization. Students should also have take the Regression and Time-Series Analysis course or be familiar with linear regression and basic statistics concepts such as standard deviation, p-values, etc.

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