Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Machine Learning Algorithms

You're reading from   Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Arrow left icon
Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838820299
Length 798 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Arrow right icon
View More author details
Toc

Table of Contents (28) Chapters Close

Preface 1. Machine Learning Model Fundamentals 2. Loss Functions and Regularization FREE CHAPTER 3. Introduction to Semi-Supervised Learning 4. Advanced Semi-Supervised Classification 5. Graph-Based Semi-Supervised Learning 6. Clustering and Unsupervised Models 7. Advanced Clustering and Unsupervised Models 8. Clustering and Unsupervised Models for Marketing 9. Generalized Linear Models and Regression 10. Introduction to Time-Series Analysis 11. Bayesian Networks and Hidden Markov Models 12. The EM Algorithm 13. Component Analysis and Dimensionality Reduction 14. Hebbian Learning 15. Fundamentals of Ensemble Learning 16. Advanced Boosting Algorithms 17. Modeling Neural Networks 18. Optimizing Neural Networks 19. Deep Convolutional Networks 20. Recurrent Neural Networks 21. Autoencoders 22. Introduction to Generative Adversarial Networks 23. Deep Belief Networks 24. Introduction to Reinforcement Learning 25. Advanced Policy Estimation Algorithms 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.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image