Book Content
chapters • 15h4m total length
1. Introducing Machine Learning
2. Managing and Understanding Data
3. Lazy Learning: Classification using Nearest Neighbors
4. Probabilistic Learning: Classification using Naïve Bayes
5. Divide and Conquer: Classification using Trees and Rules
6. Forecasting Numeric Data: Regression Methods
7. Black Box Methods: Neural Networks and Support Vector Machines
8. Finding Patterns: Market Basket Analysis Using Association Rules
9. Finding Groups of Data: Clustering with k-means
10. Evaluating Model Performance
11. Improving Model Performance
12. Specialized Machine Learning Topics
