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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 12

Starting with Simple Learners

IN THIS CHAPTER

Trying a perceptron to separate classes by a line

Partitioning recursively training data by decision trees

Discovering the rules behind playing tennis and surviving the Titanic

Leveraging Bayesian probability to analyze textual data

Beginning with this chapter, the examples start illustrating the basics of how to learn from data. The plan is to touch some of the simplest learning strategies first — providing some formulas (just those that are essential), intuitions about their functioning, and examples in R and Python for experimenting with some of their most typical characteristics. The chapter begins by reviewing the use of the perceptron to separate classes.

At the root of all principal machine learning techniques presented in the book, there is always an algorithm based on somewhat interrelated linear combinations, variations of the sample splitting of decision trees, or some kind of Bayesian probabilistic reasoning...

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83
Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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