Chapter 1: The Realm of Supervised Learning

Preprocessing data using different techniques

Building a linear regressor

Computing regression accuracy

Achieving model persistence

Building a ridge regressor

Building a polynomial regressor

Estimating housing prices

Computing the relative importance of features

Estimating bicycle demand distribution

Chapter 2: Constructing a Classifier

Building a simple classifier

Building a logistic regression classifier

Building a Naive Bayes classifier

Splitting the dataset for training and testing

Evaluating the accuracy using cross-validation

Visualizing the confusion matrix

Extracting the performance report

Evaluating cars based on their characteristics

Extracting validation curves

Extracting learning curves

Estimating the income bracket

Chapter 3: Predictive Modeling

Building a linear classifier using Support Vector Machine (SVMs)

Building a nonlinear classifier using SVMs

Extracting confidence measurements

Finding optimal hyperparameters

Building an event predictor

Chapter 4: Clustering with Unsupervised Learning

Clustering data using the k-means algorithm

Compressing an image using vector quantization

Building a Mean Shift clustering model

Grouping data using agglomerative clustering

Evaluating the performance of clustering algorithms

Automatically estimating the number of clusters using DBSCAN algorithm

Finding patterns in stock market data

Building a customer segmentation model

Chapter 5: Building Recommendation Engines

Building function compositions for data processing

Building machine learning pipelines

Finding the nearest neighbors

Constructing a k-nearest neighbors classifier

Constructing a k-nearest neighbors regressor

Computing the Euclidean distance score

Computing the Pearson correlation score

Finding similar users in the dataset

Generating movie recommendations

Chapter 6: Analyzing Text Data

Preprocessing data using tokenization

Converting text to its base form using lemmatization

Dividing text using chunking

Building a bag-of-words model

Building a text classifier

Analyzing the sentiment of a sentence

Identifying patterns in text using topic modeling

Chapter 7: Speech Recognition

Reading and plotting audio data

Transforming audio signals into the frequency domain

Generating audio signals with custom parameters

Extracting frequency domain features

Building Hidden Markov Models

Building a speech recognizer

Chapter 8: Dissecting Time Series and Sequential Data

Transforming data into the time series format

Operating on time series data

Extracting statistics from time series data

Building Hidden Markov Models for sequential data

Building Conditional Random Fields for sequential text data

Analyzing stock market data using Hidden Markov Models

Chapter 9: Image Content Analysis

Operating on images using OpenCV-Python

Detecting SIFT feature points

Building a Star feature detector

Creating features using visual codebook and vector quantization

Training an image classifier using Extremely Random Forests

Building an object recognizer

Chapter 10: Biometric Face Recognition

Capturing and processing video from a webcam

Building a face detector using Haar cascades

Building eye and nose detectors

Performing Principal Components Analysis

Performing Kernel Principal Components Analysis

Performing blind source separation

Building a face recognizer using Local Binary Patterns Histogram

Chapter 11: Deep Neural Networks

Building a single layer neural network

Building a deep neural network

Creating a vector quantizer

Building a recurrent neural network for sequential data analysis

Visualizing the characters in an optical character recognition database

Building an optical character recognizer using neural networks

Chapter 12: Visualizing Data

Plotting 3D scatter plots

Plotting date-formatted time series data

Animating dynamic signals

Chapter 13: Unsupervised Machine Learning

Principal component analysis

Introducing k-means clustering

Chapter 14: Deep Belief Networks

Neural networks – a primer

Restricted Boltzmann Machine

Chapter 15: Stacked Denoising Autoencoders

Stacked Denoising Autoencoders

Chapter 16: Convolutional Neural Networks

Chapter 17: Semi-Supervised Learning

Understanding semi-supervised learning

Semi-supervised algorithms in action

Chapter 18: Text Feature Engineering

Chapter 19: Feature Engineering Part II

Feature engineering in practice

Chapter 20: Ensemble Methods

Using models in dynamic applications

Chapter 21: Additional Python Machine Learning Tools

Alternative development tools

Chapter 22: First Steps to Scalability

Explaining scalability in detail

Python for large scale machine learning

Chapter 23: Scalable Learning in Scikit-learn

Streaming data from sources

Feature management with data streams

Chapter 24: Fast SVM Implementations

Datasets to experiment with on your own

Feature selection by regularization

Including non-linearity in SGD

Chapter 25: Neural Networks and Deep Learning

The neural network architecture

Neural networks and regularization

Neural networks and hyperparameter optimization

Neural networks and decision boundaries

Deep learning at scale with H2O

Deep learning and unsupervised pretraining

Deep learning with theanets

Autoencoders and unsupervised learning

Chapter 26: Deep Learning with TensorFlow

Machine learning on TensorFlow with SkFlow

Keras and TensorFlow installation

Convolutional Neural Networks in TensorFlow through Keras

CNN's with an incremental approach

Chapter 27: Classification and Regression Trees at Scale

Random forest and extremely randomized forest

Fast parameter optimization with randomized search

Out-of-core CART with H2O

Chapter 28: Unsupervised Learning at Scale

Feature decomposition – PCA

Chapter 29: Distributed Environments – Hadoop and Spark

From a standalone machine to a bunch of nodes

Chapter 30: Practical Machine Learning with Spark

Setting up the VM for this chapter

Sharing variables across cluster nodes

Data preprocessing in Spark

Machine learning with Spark