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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

Averaging Models

Machine learning involves building many models and creating many different predictions, all with different expected error performances. It may surprise you to know that you can get even better results by averaging the models together. The principle is quite simple: Estimate variance is random, so by averaging many different models, you can enhance the signal (the correct prediction) and rule out the noise that will often cancel itself (opposite errors sum to zero).

remember Sometimes the results from an algorithm that performs well, mixed with the results from a simpler algorithm that doesn’t work as well, can create better predictions than using a single algorithm. Don’t underestimate contributions delivered from simpler models, such as linear models, when you average their results with the output from more sophisticated algorithms, such as gradient boosting.

It’s the same principle you seek when applying ensembles of learners, such as tree bagging and boosting...

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83
Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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