1. What is machine learning? It is common sense, except done by a computer
2. Types of machine learning
3. Drawing a line close to our points : linear regression
4. Optimizing the training process : underfitting, overfitting, testing, and regularization
5. Using lines to split our points: the perception algorithm
6. A continuous approach to splitting points : logistic classifiers
7. How do you measure classification models? Accuracy and its friends
8. Using probability to its maximum : the naive Bayes model
9. Splitting data by asking questions : decision trees
10. Combining building blocks to gain more power : neural networks
11. Finding boundaries with style : support vector machines and the kernel method
12. Combining models to maximize results : ensemble learning
13. Putting it all in practice : a real-life example of data engineering and machine learning
