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.

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Table of Contents (21 chapters)
Preface
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
Get in touch
Free Chapter
1
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
Summary
2
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
Summary
3
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
Summary
4
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
Summary
5
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
Summary
6
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
Summary
7
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
Summary
8
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
Summary
9
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
Summary
10
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
Summary
11
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
Summary
12
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
Summary
13
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
Summary
14
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
Summary
15
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
Summary
16
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
Summary
17
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
Summary
18
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
19
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20
Index
Index

Using Python for machine learning

Python is one of the most popular programming languages for data science and thanks to its very active developer and open source community, a large number of useful libraries for scientific computing and machine learning have been developed.

Although the performance of interpreted languages, such as Python, for computation-intensive tasks is inferior to lower-level programming languages, extension libraries such as NumPy and SciPy have been developed that build upon lower-layer Fortran and C implementations for fast vectorized operations on multidimensional arrays.

For machine learning programming tasks, we will mostly refer to the scikit-learn library, which is currently one of the most popular and accessible open source machine learning libraries. In the later chapters, when we focus on a subfield of machine learning called deep learning, we will use the latest version of the TensorFlow library, which specializes in training so-called deep neural network models very efficiently by utilizing graphics cards.

Installing Python and packages from the Python Package Index

Python is available for all three major operating systems—Microsoft Windows, macOS, and Linux—and the installer, as well as the documentation, can be downloaded from the official Python website: https://www.python.org.

This book is written for Python version 3.7 or higher, and it is recommended that you use the most recent version of Python 3 that is currently available. Some of the code may also be compatible with Python 2.7, but as the official support for Python 2.7 ends in 2019, and the majority of open source libraries have already stopped supporting Python 2.7 (https://python3statement.org), we strongly advise that you use Python 3.7 or newer.

The additional packages that we will be using throughout this book can be installed via the pip installer program, which has been part of the Python Standard Library since Python 3.3. More information about pip can be found at https://docs.python.org/3/installing/index.html.

After we have successfully installed Python, we can execute pip from the terminal to install additional Python packages:

pip install SomePackage

Already installed packages can be updated via the --upgrade flag:

pip install SomePackage --upgrade

Using the Anaconda Python distribution and package manager

A highly recommended alternative Python distribution for scientific computing is Anaconda by Continuum Analytics. Anaconda is a free—including commercial use—enterprise-ready Python distribution that bundles all the essential Python packages for data science, math, and engineering into one user-friendly, cross-platform distribution. The Anaconda installer can be downloaded at https://docs.anaconda.com/anaconda/install/, and an Anaconda quick start guide is available at https://docs.anaconda.com/anaconda/user-guide/getting-started/.

After successfully installing Anaconda, we can install new Python packages using the following command:

conda install SomePackage

Existing packages can be updated using the following command:

conda update SomePackage

Packages for scientific computing, data science, and machine learning

Throughout this book, we will mainly use NumPy's multidimensional arrays to store and manipulate data. Occasionally, we will make use of pandas, which is a library built on top of NumPy that provides additional higher-level data manipulation tools that make working with tabular data even more convenient. To augment your learning experience and visualize quantitative data, which is often extremely useful to make sense of it, we will use the very customizable Matplotlib library.

The version numbers of the major Python packages that were used to write this book are mentioned in the following list. Please make sure that the version numbers of your installed packages are equal to, or greater than, these version numbers to ensure that the code examples run correctly:

  • NumPy 1.17.4
  • SciPy 1.3.1
  • scikit-learn 0.22.0
  • Matplotlib 3.1.0
  • pandas 0.25.3
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