Book Content
chapters • 7h44m total length
1. Thinking in Machine Learning
2. Tools Overview
3. Turning data into information
4. Models: Learning from information
5. Linear Models
6. Neural nets
7. Features: How algorithms see the world
8. Learning with ensembles
9. Some real world examples
