目录 Preface Chapter 1: Introduction to Artificial Intelligence What is Artificial Intelligence? Why do we need to study AI? Applications of AI Branches of AI Defining intelligence using Turing Test Making machines think like humans Building rational agents General Problem Solver Solving a problem with GPS Building an intelligent agent Types of models Installing Python 3 Installing on Ubuntu Installing on Mac OS X Installing on Windows Installing packages Loading data Summary Chapter 2: Classification and Regression Using Supervised Learning Supervised versus unsupervised learning What is classification? Preprocessing data Binarization Mean removal Scaling Normalization Label encoding Logistic Regression classifier Naive Bayes classifier Confusion matrix Support Vector Machines Classifying income data using Support Vector Machines What is Regression? Building a single variable regressor Building a multivariable regressor Estimating housing prices using a Support Vector Regressor Summary Chapter 3: Predictive Analytics with Ensemble Learning What is Ensemble Learning? Building learning models with Ensemble Learning What are Decision Trees? Building a Decision Tree classifier What are Random Forests and Extremely Random Forests? Building Random Forest and Extremely Random Forest classifiers Estimating the confidence measure of the predictions Dealing with class imbalance Finding optimal training parameters using grid search Computing relative feature importance