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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

Applying Feature Engineering

If you believe that bias is still affecting your model, you have little choice but to create new features that improve the model’s performance. Every new feature can make guessing the target response easier. For instance, if classes aren’t linearly separable, feature creation is the only way to change a situation that your machine learning algorithm cannot properly deal with.

Automatic feature creation is possible using polynomial expansion or the support vector machines class of machine learning algorithms. Support vector machines can automatically look for better features in higher-dimensional feature spaces in a way that’s both computationally fast and memory optimal.

However, nothing can really substitute for your expertise and understanding of the method needed to solve the data problem that the algorithm is trying to learn. You can create features based on your knowledge and ideas of how things work in the world. Humans are still...

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83
Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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