Python Machine Learning Cookbook - Second Edition

Python Machine Learning Cookbook - Second Edition

This is the code repository for Python Machine Learning Cookbook - Second Edition , published by Packt.

Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets

What is this book about?

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.

With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.

By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

This book covers the following exciting features:

If you feel this book is for you, get your copy today!

<img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" alt="https://www.packtpub.com/" border="5" />

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

import numpy as np
import matplotlib.pyplot as plt

X = np.array([[3,1], [2,5], [1,8], [6,4], [5,2], [3,5], [4,7], [4,-1]])

Following is what you need for this book: This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.

With the following software and hardware list you can run all code files present in the book (Chapter 1-16).

Software and Hardware List

Chapter Software required OS required
All Python 3.6 Windows, Mac OS X, and Linux (Any)
All scikit-learn Keras Windows
7 TensorFlow Windows
All MLBox Linux
9-16 OpenCV Linux

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Related products

Get to Know the Author

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the UniversitÓ degli Studi della Campania Luigi Vanvitelli, Italy. He has over 15 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.

Prateek Joshi is an artificial intelligence researcher, an author of several books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up building an intelligence platform for water facilities. He graduated from the University of Southern California with a Master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research.

Other books by the authors

Python Machine Learning Cookbook

Keras 2.x Projects

Suggestions and Feedback

Click here if you have any feedback or suggestions.