Liu - Python machine learning by example: easy-to-follow examples that get you up and running with machine learning

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First published: May 2017
Second edition: February 2019

Production reference: 1270219

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ISBN 978-1-78961-672-9

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This book is a deep dive into the exciting world of machine learning. What's unique about this book is the clarity with which it explains concepts from first principles and teaches by example in a way that is accessible to a wide audience. You will learn how to implement key algorithms from scratch and compare your code against proven machine learning libraries.

The discussion in this book is backed by mathematical principles and includes from-scratch coding exercises that help you gain a deeper understanding of the subject. By reading this book, you will be learning something new, whether you are a beginner or an experienced machine learning practitioner.
True to its title, you will learn about a number of interesting applications, such as predicting click-through rates for targeted advertisements, mining text data for patterns, and predicting the stock price of a major exchange index. Throughout the book, you will find exercises and links to help you better understand the material.
I encourage you to turn the page and dive into the exciting world of machine learning.

Vadim Smolyakov

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.

Vadim Smolyakov is currently pursuing his PhD at MIT in the areas of computer science and artificial intelligence. His primary research interests include Bayesian inference, deep learning, and optimization. Prior to coming to MIT, Vadim received his undergraduate degree in engineering science at the University of Toronto. He previously worked as a data scientist in the e-commerce space. Vadim is passionate about machine learning and data science, and is interested in making the field accessible to a broad audience and inspiring readers to innovate and pursue research in artificial intelligence.

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The surge in interest in machine learning 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 machine learning, this book will serve as your entry point.