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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

Training, Validating, and Testing

In a perfect world, you could perform a test on data that your machine learning algorithm has never learned from before. However, waiting for fresh data isn’t always feasible in terms of time and costs. As a first simple remedy, you can randomly split your data into training and test sets. The common split is from 25 to 30 percent for testing and the remaining 75 to 70 percent for training. You split your data consisting of your response and features at the same time, keeping correspondence between each response and its features.

The second remedy occurs when you need to tune your learning algorithm. In this case, the test split data isn’t a good practice because it causes another kind of overfitting called snooping (see more on this topic later in the chapter). To overcome snooping, you need a third split, called a validation set. A suggested split is to have your examples partitioned in thirds: 70 percent for training, 20 percent for validation...

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83
Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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