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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 18

Resorting to Ensembles of Learners

IN THIS CHAPTER

Discovering why many guesses are better than one

Making uncorrelated trees work well together in Random Forests

Learning to map complex target functions piece by piece using boosting

Getting better predictions by averaging models

After discovering so many complex and powerful algorithms, you might be surprised to discover that a summation of simpler machine learning algorithms can often outperform the most sophisticated solutions. Such is the power of ensembles, groups of models made to work together to produce better predictions. The amazing thing about ensembles is that they are made up of groups of singularly nonperforming algorithms.

Ensembles don’t work much differently from the collective intelligence of crowds, through which a set of wrong answers, if averaged, provides the right answer. Sir Francis Galton, the English Victorian age statistician known for having formulated the idea of correlation, narrated the...

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Tech Concepts
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Programming languages
73
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Machine Learning For Dummies
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