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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 22

Ten Machine Learning Packages to Master

IN THIS CHAPTER

Analyzing live-streaming data using Cloudera Oryx

Recognizing objects in images using CUDA-Convnet

Adding image recognition to web-based apps using ConvNetJS

Obtaining an R SVM implementation using e1071

Adding R GBM optimization support using gbm

Performing natural language interface tasks using Gensim

Creating a generalized linear model using glmnet

Growing a forest of R decision trees using randomForest

Performing scientific tasks of all sorts in Python using SciPy

Obtaining GBDT, GBRT, and GBM support for a variety of languages with XGBoost

The book provides you with a wealth of information about specific machine learning packages such as caret (R) and NumPy (Python). Of course, these are good, versatile packages you can use to begin your machine learning journey. It’s important to have more than a few tools in your toolbox, which is where the suggestions found in this chapter come into play. These packages...

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Tech Concepts
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Programming languages
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Machine Learning For Dummies
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