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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

Learning One Example at a Time

Finding the right coefficients for a linear model is just a matter of time and memory. However, sometimes a system won’t have enough memory to store a huge dataset. In this case, you must resort to other means, such as learning from one example at a time rather than having all of them loaded into memory. The following sections help you understand the one-example-at-a-time approach to learning.

Using gradient descent

The gradient descent finds the right way to minimize the cost function one iteration at a time. After each step, it accounts for all the model’s summed errors and updates the coefficients in order to make the error even smaller during the next data iteration. The efficiency of this approach derives from considering all the examples in the sample. The drawback of this approach is that you must load all the data into memory.

Unfortunately, you can’t always store all the data in memory because some datasets are huge. In addition...

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83
Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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