Practical Machine Learning

5 hours to complete
Flexible Schedule

Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD

What You’ll Learn

Use the basic components of building and applying prediction functions

Understand concepts such as training and tests sets, overfitting, and error rates

Describe machine learning methods such as regression or classification trees

Explain the complete process of building prediction functions

Skills You’ll Gain

Regression Analysis Decision Tree Learning Classification And Regression Tree (CART) Statistical Machine Learning Feature Engineering Data Collection Random Forest Algorithm Applied Machine Learning Machine Learning Algorithms Supervised Learning Data Processing Predictive Modeling Machine Learning

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile.

Develop Your Specialized Knowledge

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

There are 4 modules in this course

This week will cover prediction, relative importance of steps, errors, and cross validation.

This week will introduce the caret package, tools for creating features and preprocessing.

This week we introduce a number of machine learning algorithms you can use to complete your course project.

This week, we will cover regularized regression and combining predictors.