Table of Contents

Introducing Machine Learning

Managing and Understanding Data

Lazy Learning – Classification Using Nearest Neighbors

Probabilistic Learning – Classification Using Naive Bayes

Divide and Conquer – Classification Using Decision Trees and Rules

Forecasting Numeric Data – Regression Methods

Black Box Methods – Neural Networks and Support Vector Machines

Finding Patterns – Market Basket Analysis Using Association Rules

Finding Groups of Data – Clustering with k-means

Evaluating Model Performance

Improving Model Performance

Specialized Machine Learning Topics

About the Author

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.