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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