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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 23

Ten Ways to Improve Your Machine Learning Models

IN THIS CHAPTER

Understanding the best ways to improve your model

Avoiding snooping and other self-deceptions

Figuring out the best ways to optimize your problem

Getting the right parameters for the best result

Exploring solutions from simplest to most complex

Putting different solutions together

Using one solution to predict another

Creating and engineering new features

Setting less useful features and variables apart

Offering algorithms more chances to learn

Now that your algorithm has finished learning from the data obtained using Python or R, you’re pondering the results from your test set and wondering whether you can improve them or have really reached the best possible outcome. This chapter introduces you to a number of checks and actions that hint at methods you can use to improve machine learning performance and achieve a more general predictor that’s able to work equally well with your test set or new...

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83
Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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