Python Machine Learning By Example - Second Edition: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition Contributor(s): (Hayden) Liu, Yuxi (Author (Hayden) Liu, Yuxi (Author) |
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ISBN: 1789616727 ISBN-13: 9781789616729 Publisher: Packt Publishing OUR PRICE: $37.04 Product Type: Paperback - Other Formats Published: February 2019 |
Additional Information |
BISAC Categories: - Computers | Programming Languages - Python - Computers | Databases - Data Mining - Computers | Machine Theory |
Physical Information: 0.79" H x 7.5" W x 9.25" (1.44 lbs) 382 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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
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
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. |