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  • Book Overview & Buying Machine Learning For Dummies
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Machine Learning For Dummies

Machine Learning For Dummies

By : John Paul Mueller, Luca Massaron
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Machine Learning For Dummies

Machine Learning For Dummies

By: John Paul Mueller, Luca Massaron

Overview of this book

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. In the initial chapters, the book introduces you to the world of machine learning, artificial intelligence, big data, and will prepare you to use R and Python for machine learning tasks. Next, you’ll learn how to use math in machine learning and get started with linear models and neural networks. In the final chapters, you’ll process images and text, and discover packages and techniques to improve your machine learning models. By the end of this book, you’ll be able to understand and implement machine learning seamlessly.
Table of Contents (34 chapters)
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2
Part 1: Introducing How Machines Learn
6
Part 2: Preparing Your Learning Tools
12
Part 3: Getting Started with the Math Basics
17
Part 4: Learning from Smart and Big Data
24
Part 5: Applying Learning to Real Problems
28
Part 6: The Part of Tens
31
About the Author
32
Advertisement Page
33
Connect with Dummies
34
End User License Agreement

Chapter 14

Leveraging Similarity

IN THIS CHAPTER

Understanding differences between examples

Clustering data into meaningful groups

Classifying and regressing after looking for data neighbors

Grasping the difficulties of working in a high-dimensional data space

A rose is a rose. A tree is a tree. A car is a car. Even though you can make simple statements like this, one example of each kind of item doesn’t suffice to identify all the items that fit into that classification. After all, many species of trees and many kinds of roses exist. If you evaluate the problem under a machine learning framework in the examples, you find features whose values change frequently and features that somehow systematically persist (a tree is always made of wood and has a trunk and roots, for instance). When you look closely for the features’ values that repeat constantly, you can guess that certain observed objects are of much the same kind.

So, children can figure out by themselves what cars...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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