- Choosing a classification algorithm
- First steps with scikit-learn -- training a perceptron
- Modeling class probabilities via logistic regression
- Logistic regression intuition and conditional probabilities
- Learning the weights of the logistic cost function
- Converting an Adaline implementation into an algorithm for logistic regression
- Training a logistic regression model with scikit-learn
- Tackling overfitting via regularization
- Maximum margin classification with support vector machines
- Maximum margin intuition
- Dealing with a nonlinearly separable case using slack variables
- Alternative implementations in scikit-learn
- Solving nonlinear problems using a kernel SVM
- Kernel methods for linearly inseparable data
- Using the kernel trick to find separating hyperplanes in high-dimensional space
- Decision tree learning
- Maximizing information gain – getting the most bang for your buck
- Building a decision tree
- Combining multiple decision trees via random forests
- K-nearest neighbors – a lazy learning algorithm
- Summary
Please refer to the README.md file in ../ch01 for more information about running the code examples.