Machine Learning (in Python and R) For Dummies

John Paul Mueller and Luca Massaron

ISBN 10: 8126563052 / ISBN 13: 9788126563050

Published by Wiley, 2016

About this Item

Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation and email spam filtering wouldn't be possible and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.

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Bibliographic Details

• Title: Machine Learning (in Python and R) For Dummies
• Publisher: Wiley
• Publication Date: 2016
• Language: English
• ISBN 10: 8126563052
• ISBN 13: 9788126563050
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Brand New International Paper-back Edition same as per description, **Economy edition, May have been printed in Asia with cover stating Not for sale in US. Legal to use despite any disclaimer on cover. Save Money. Contact us for any queries. Best Customer Support! All Orders shipped with Tracking Number.

From the Back Cover

• Learn how machine learning algorithms are invaluable
• Implement machine learning in Python and R
• Use machine learning to accomplish practical tasks

Machine learning made easy!

It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. It explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible, specifies how to do practical things using common algorithms, and more!

Inside...

• The real story about AI
• R and Python coding
• The role of statistics
• Managing big data
• Demystifying the math
• Using linear models
• What neural networks do
• How to work with images
• More tools you can use