Python Machine Learning By Example Second Edition

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

Implement machine learning algorithms and techniques to build intelligent systems

What is this book about?

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.

Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.

With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.

By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.

This book covers the following exciting features:

Exploit the power of Python to explore the world of data mining and data analytics
Discover machine learning algorithms to solve complex challenges faced by data scientists today
Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects

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

Instructions and Navigations

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

The code will look like the following:

with open(file_path, 'r') as infile:
...    spam_sample = infile.read()

Following is what you need for this book: If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.

With the following software and hardware list you can run all code files present in the book (Chapter 1-10).

Software and Hardware List

Chapter	Software required	OS required
All	Python 3.6 or later	Windows, Mac OS X, and Linux (Any)
All	Jupyter Notebook	Windows, Mac OS X, and Linux (Any)
All	Apache Spark	Windows, Mac OS X, and Linux (Any)
All	Scala	Windows, Mac OS X, and Linux (Any)

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

Get to Know the Author

Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller in Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt. He is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.

Another book by the author

R Deep Learning Projects

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