Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
Other Books You May Enjoy
27
Index

To get the most out of this book

  • The reader must possess a basic knowledge of the most common machine learning algorithms, with a clear understanding of their mathematical structure and applications.
  • As Python is the language chosen for the example, the reader must be familiar with this language and, in particular, frameworks like scikit-learn, TensorFlow 2, pandas, and PyStan.
  • Considering the complexity of some topics, a good knowledge of calculus, probability theory, linear algebra, and statistics is strongly advised.

Download the example code files

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

You can download the code files by following these steps:

  1. Log in or register at http://www.packt.com.
  2. Select the Support tab.
  3. Click on Code Download.
  4. Enter the name of the book in the Search box and follow the on-screen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR / 7-Zip for Windows
  • Zipeg / iZip / UnRarX for Mac
  • 7-Zip / PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Mastering-Machine-Learning-Algorithms-Second-Edition. 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 a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781838820299_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example, "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

ax[0].set_title('L1 regularization', fontsize=18)
ax[0].set_xlabel('Parameter', fontsize=18)
ax[0].set_ylabel(r'$|\theta_i|$', fontsize=18)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

ax[0].set_title('L1 regularization', fontsize=18)
ax[0].set_xlabel('Parameter', fontsize=18)
ax[0].set_ylabel(r'$|\theta_i|$', fontsize=18)

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

pip install -U scikit-fuzzy

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.