ch03.toc

Choosing a classification algorithm
First steps with scikit-learn
    Training a perceptron via scikit-learn
Modeling class probabilities via logistic regression
    Logistic regression intuition and conditional probabilities
    Learning the weights of the logistic cost function
    Training a logistic regression model with scikit-learn
    Tackling overfitting via regularization
Maximum margin classification with support vector machines
    Maximum margin intuition
    Dealing with the nonlinearly separable case using slack variables
    Alternative implementations in scikit-learn
Solving nonlinear problems using a kernel SVM
    Using the kernel trick to find separating hyperplanes in higher dimensional space
Decision tree learning
    Maximizing information gain – getting the most bang for the buck
    Building a decision tree
    Combining weak to strong learners via random forests
K-nearest neighbors – a lazy learning algorithm
Summary