Sebastian Raschka, 2015
Python Machine Learning - Code Examples
- 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