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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

Using Cross-Validation Correctly

Seeing a large difference between the cross-validation (CV) estimates and the result is a common problem that appears with a test set or fresh data. Having this problem means that something went wrong with the cross-validation. Beyond the fact that CV isn’t a good performance predictor, this problem also means that a misleading indicator has induced you to model the problem incorrectly and achieve unsatisfactory results.

remember Cross-validation provides you with hints when the steps you take (data preparation, data and feature selection, hyper-parameter fixing, or model selection) are correct. It’s important, but not critical, that CV estimates precisely replicate out-of-sample error measurements. However, it is crucial that CV estimates correctly reflect improvement or worsening in the test phase due to your modeling decisions. Generally, there are two reasons that the cross-validation estimates can vary from the true error results:

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Machine Learning For Dummies
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