Chapter 1: Giving Computers the Ability to Learn from Data

Building intelligent machines to transform data into knowledge

The three different types of machine learning

An introduction to the basic terminology and notations

A roadmap for building machine learning systems

Using Python for machine learning

Chapter 2: Training Machine Learning Algorithms for Classification

Artificial neurons – a brief glimpse into the early history of machine learning

Implementing a perceptron learning algorithm in Python

Adaptive linear neurons and the convergence of learning

Chapter 3: A Tour of Machine Learning Classifiers Using Scikit-learn

Choosing a classification algorithm

First steps with scikit-learn

Modeling class probabilities via logistic regression

Maximum margin classification with support vector machines

Solving nonlinear problems using a kernel SVM

K-nearest neighbors – a lazy learning algorithm

Chapter 4: Building Good Training Sets – Data Preprocessing

Dealing with missing data

Handling categorical data

Partitioning a dataset in training and test sets

Bringing features onto the same scale

Selecting meaningful features

Assessing feature importance with random forests

Chapter 5: Compressing Data via Dimensionality Reduction

Unsupervised dimensionality reduction via principal component analysis

Supervised data compression via linear discriminant analysis

Using kernel principal component analysis for nonlinear mappings

Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Streamlining workflows with pipelines

Using k-fold cross-validation to assess model performance

Debugging algorithms with learning and validation curves

Fine-tuning machine learning models via grid search

Looking at different performance evaluation metrics

Chapter 7: Combining Different Models for Ensemble Learning

Implementing a simple majority vote classifier

Evaluating and tuning the ensemble classifier

Bagging – building an ensemble of classifiers from bootstrap samples

Leveraging weak learners via adaptive boosting

Chapter 8: Applying Machine Learning to Sentiment Analysis

Obtaining the IMDb movie review dataset

Introducing the bag-of-words model

Training a logistic regression model for document classification

Working with bigger data – online algorithms and out-of-core learning

Chapter 9: Embedding a Machine Learning Model into a Web Application

Serializing fitted scikit-learn estimators

Setting up a SQLite database for data storage

Developing a web application with Flask

Turning the movie classifier into a web application

Deploying the web application to a public server

Chapter 10: Predicting Continuous Target Variables with Regression Analysis

Introducing a simple linear regression model

Exploring the Housing Dataset

Implementing an ordinary least squares linear regression model

Fitting a robust regression model using RANSAC

Evaluating the performance of linear regression models

Using regularized methods for regression

Turning a linear regression model into a curve – polynomial regression

Chapter 11: Working with Unlabeled Data – Clustering Analysis

Grouping objects by similarity using k-means

Organizing clusters as a hierarchical tree

Locating regions of high density via DBSCAN

Chapter 12: Training Artificial Neural Networks for Image Recognition

Modeling complex functions with artificial neural networks

Classifying handwritten digits

Training an artificial neural network

Developing your intuition for backpropagation

Debugging neural networks with gradient checking

Convergence in neural networks

Other neural network architectures

A few last words about neural network implementation

Chapter 13: Parallelizing Neural Network Training with Theano

Building, compiling, and running expressions with Theano

Choosing activation functions for feedforward neural networks

Training neural networks efficiently using Keras

Chapter 14: Thinking in Machine Learning

Chapter 15: Tools and Techniques

Python for machine learning

Installing the SciPy stack

Chapter 16: Turning Data into Information

Chapter 17: Models – Learning from Information

Chapter 18: Linear Models

Introducing least squares

Multiclass classification

Chapter 19: Neural Networks

Getting started with neural networks

Implementing a neural network

Other neural net architectures

Chapter 20: Features – How Algorithms See the World

Operations and statistics

Principle component analysis

Chapter 21: Learning with Ensembles

Chapter 22: Design Strategies and Case Studies

Evaluating model performance

Machine learning at a glance

Chapter 23: Unsupervised Machine Learning

Principal component analysis

Introducing k-means clustering

Chapter 24: Deep Belief Networks

Neural networks – a primer

Restricted Boltzmann Machine

Chapter 25: Stacked Denoising Autoencoders

Stacked Denoising Autoencoders

Chapter 26: Convolutional Neural Networks

Chapter 27: Semi-Supervised Learning

Understanding semi-supervised learning

Semi-supervised algorithms in action

Chapter 28: Text Feature Engineering

Chapter 29: Feature Engineering Part II

Feature engineering in practice

Chapter 30: Ensemble Methods

Using models in dynamic applications

Chapter 31: Additional Python Machine Learning Tools

Alternative development tools

Chapter 32: Chapter Code Requirements