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
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