Book Content
chapters • 15h16m total length
1. Introducing Machine Learning
2. Managing and Understanding Data
3. Lazy Learning – Classification Using Nearest Neighbors
4. Probabilistic Learning – Classification Using Naive Bayes
5. Divide and Conquer – Classification Using Decision 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
