Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition

(Hayden) Liu, Yuxi

ISBN 10: 1789616727 / ISBN 13: 9781789616729

Published by Packt Publishing, 2019

About this Item

In.

Seller Inventory # ria9781789616729_new

Bibliographic Details

• Title: Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition
• Publisher: Packt Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 1789616727
• ISBN 13: 9781789616729
• Binding: Soft cover
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn

Key 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

Book Description

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.

What you will learn

• Understand the important concepts in machine learning and data science
• Use Python to explore the world of data mining and analytics
• Scale up model training using varied data complexities with Apache Spark
• Delve deep into text and NLP using Python libraries such NLTK and gensim
• Select and build an ML model and evaluate and optimize its performance
• Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn

Who this book is for

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.

About the Author

Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon in 2017 and 2018. His other books include R Deep Learning Projects, Hands-On Deep Learning Architectures with Python, and second edition of Python Machine Learning by Example. 

He is an experienced machine learning scientist focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and applied his ML expertise in computational advertising, where he developed ad bidding and targeting algorithms based on Reinforcement Learning techniques. He published five first-authored IEEE transaction and conference papers during his master's research in University of Toronto.