Book Image

Python Machine Learning - Third Edition

By : Sebastian Raschka, Vahid Mirjalili
Book Image

Python Machine Learning - Third Edition

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.
Table of Contents (21 chapters)
20
Index

To get the most out of this book

Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.

Download the example code files

You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files emailed directly to you.

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  2. Select the Support tab.
  3. Click on Code Downloads.
  4. Enter the name of the book in the Search box and follow the on-screen instructions.

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Alternatively, if you have obtained a copy of the book from elsewhere or do not wish to create an account at Packt, all code examples are also available for download through GitHub at https://github.com/rasbt/python-machine-learning-book-3rd-edition.

All code in this book is also available in the form of Jupyter notebooks, and a short introduction can be found in the code directory of Chapter 1, Giving Computers the Ability to Learn from Data, at https://github.com/rasbt/python-machine-learning-book-3rd-edition/tree/master/ch01#pythonjupyter-notebook. For more information about the general Jupyter Notebook GUI, please see the official documentation at https://jupyter-notebook.readthedocs.io/en/stable/.

While we recommend using Jupyter Notebook for executing code interactively, all code examples are available in both a Python script (for example, ch02/ch02.py) and a Jupyter Notebook format (for example, ch02/ch02.ipynb). Furthermore, we recommend that you view the README.md file that accompanies each individual chapter for additional information and updates (for example, https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/ch01/README.md).

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide you with a PDF file that has color images of the screenshots/diagrams used in this book. The color images will help you to better understand the changes in the output. You can download this file from https://static.packt-cdn.com/downloads/9781789955750_ColorImages.pdf. In addition, lower resolution color images are embedded in the code notebooks of this book that come bundled with the example code files.

Conventions used

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text are shown as follows: "And already installed packages can be updated via the --upgrade flag."

A block of code is set as follows:

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> y = df.iloc[0:100, 4].values
>>> y = np.where(y == 'Iris-setosa', -1, 1)
>>> X = df.iloc[0:100, [0, 2]].values
>>> plt.scatter(X[:50, 0], X[:50, 1],
...             color='red', marker='x', label='setosa')
>>> plt.scatter(X[50:100, 0], X[50:100, 1],
...             color='blue', marker='o', label='versicolor')
>>> plt.xlabel('sepal length')
>>> plt.ylabel('petal length')
>>> plt.legend(loc='upper left')
>>> plt.show()

Any command-line input or output is written as follows:

> dot -Tpng tree.dot -o tree.png

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."

Warnings or important notes appear in a box like this.

Tips and tricks appear like this.