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