Book Image

Python Machine Learning - Third Edition

By : Sebastian Raschka, Vahid Mirjalili

5 (1)

Book Image

Python Machine Learning - Third Edition

5 (1)

By: Sebastian Raschka, Vahid Mirjalili

Overview of this book

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

Preface

Get started with machine learning

Who this book is for

What this book covers

What you need for this book

To get the most out of this book

Free Chapter

Giving Computers the Ability to Learn from Data

Giving Computers the Ability to Learn from Data

Building intelligent machines to transform data into knowledge

The three different types of machine learning

Introduction to the basic terminology and notations

A roadmap for building machine learning systems

Using Python for machine learning

Training Simple Machine Learning Algorithms for Classification

Training Simple 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

A Tour of Machine Learning Classifiers Using scikit-learn

A Tour of Machine Learning Classifiers Using scikit-learn

Choosing a classification algorithm

First steps with scikit-learn – training a perceptron

Modeling class probabilities via logistic regression

Maximum margin classification with support vector machines

Solving nonlinear problems using a kernel SVM

Decision tree learning

K-nearest neighbors – a lazy learning algorithm

Building Good Training Datasets – Data Preprocessing

Building Good Training Datasets – Data Preprocessing

Dealing with missing data

Handling categorical data

Partitioning a dataset into separate training and test datasets

Bringing features onto the same scale

Selecting meaningful features

Assessing feature importance with random forests

Compressing Data via Dimensionality Reduction

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

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

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

Combining Different Models for Ensemble Learning

Combining Different Models for Ensemble Learning

Learning with ensembles

Combining classifiers via majority vote

Bagging – building an ensemble of classifiers from bootstrap samples

Leveraging weak learners via adaptive boosting

Applying Machine Learning to Sentiment Analysis

Applying Machine Learning to Sentiment Analysis

Preparing the IMDb movie review data for text processing

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

Topic modeling with Latent Dirichlet Allocation

Embedding a Machine Learning Model into a Web Application

Embedding a Machine Learning Model into a Web Application

Serializing fitted scikit-learn estimators

Setting up an SQLite database for data storage

Developing a web application with Flask

Turning the movie review classifier into a web application

Deploying the web application to a public server

Predicting Continuous Target Variables with Regression Analysis

Predicting Continuous Target Variables with Regression Analysis

Introducing linear regression

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

Dealing with nonlinear relationships using random forests

Working with Unlabeled Data – Clustering Analysis

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

Implementing a Multilayer Artificial Neural Network from Scratch

Implementing a Multilayer Artificial Neural Network from Scratch

Modeling complex functions with artificial neural networks

Classifying handwritten digits

Training an artificial neural network

About the convergence in neural networks

A few last words about the neural network implementation

Parallelizing Neural Network Training with TensorFlow

Parallelizing Neural Network Training with TensorFlow

TensorFlow and training performance

First steps with TensorFlow

Building input pipelines using tf.data – the TensorFlow Dataset API

Building an NN model in TensorFlow

Choosing activation functions for multilayer neural networks

Going Deeper – The Mechanics of TensorFlow

Going Deeper – The Mechanics of TensorFlow

The key features of TensorFlow

TensorFlow's computation graphs: migrating to TensorFlow v2

TensorFlow Variable objects for storing and updating model parameters

Computing gradients via automatic differentiation and GradientTape

Simplifying implementations of common architectures via the Keras API

TensorFlow Estimators

Classifying Images with Deep Convolutional Neural Networks

Classifying Images with Deep Convolutional Neural Networks

The building blocks of CNNs

Putting everything together – implementing a CNN

Implementing a deep CNN using TensorFlow

Gender classification from face images using a CNN

Modeling Sequential Data Using Recurrent Neural Networks

Modeling Sequential Data Using Recurrent Neural Networks

Introducing sequential data

RNNs for modeling sequences

Implementing RNNs for sequence modeling in TensorFlow

Understanding language with the Transformer model

Generative Adversarial Networks for Synthesizing New Data

Generative Adversarial Networks for Synthesizing New Data

Introducing generative adversarial networks

Implementing a GAN from scratch

Improving the quality of synthesized images using a convolutional and Wasserstein GAN

Other GAN applications

Reinforcement Learning for Decision Making in Complex Environments

Reinforcement Learning for Decision Making in Complex Environments

Introduction – learning from experience

The theoretical foundations of RL

Reinforcement learning algorithms

Implementing our first RL algorithm

Chapter and book summary

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Summary

In this chapter, you learned about three different, fundamental dimensionality reduction techniques for feature extraction: standard PCA, LDA, and KPCA. Using PCA, we projected data onto a lower-dimensional subspace to maximize the variance along the orthogonal feature axes, while ignoring the class labels. LDA, in contrast to PCA, is a technique for supervised dimensionality reduction, which means that it considers class information in the training dataset to attempt to maximize the class-separability in a linear feature space.

Lastly, you learned about a nonlinear feature extractor, KPCA. Using the kernel trick and a temporary projection into a higher-dimensional feature space, you were ultimately able to compress datasets consisting of nonlinear features onto a lower-dimensional subspace where the classes became linearly separable.

Equipped with these essential preprocessing techniques, you are now well prepared to learn about the best practices for efficiently incorporating...