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Python Machine Learning Cookbook (Paperback)
Loot Price: R1,674
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Python Machine Learning Cookbook (Paperback)
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100 recipes that teach you how to perform various machine learning
tasks in the real world About This Book * Understand which
algorithms to use in a given context with the help of this exciting
recipe-based guide * Learn about perceptrons and see how they are
used to build neural networks * Stuck while making sense of images,
text, speech, and real estate? This guide will come to your rescue,
showing you how to perform machine learning for each one of these
using various techniques Who This Book Is For This book is for
Python programmers who are looking to use machine-learning
algorithms to create real-world applications. This book is friendly
to Python beginners, but familiarity with Python programming would
certainly be useful to play around with the code. What You Will
Learn * Explore classification algorithms and apply them to the
income bracket estimation problem * Use predictive modeling and
apply it to real-world problems * Understand how to perform market
segmentation using unsupervised learning * Explore data
visualization techniques to interact with your data in diverse ways
* Find out how to build a recommendation engine * Understand how to
interact with text data and build models to analyze it * Work with
speech data and recognize spoken words using Hidden Markov Models *
Analyze stock market data using Conditional Random Fields * Work
with image data and build systems for image recognition and
biometric face recognition * Grasp how to use deep neural networks
to build an optical character recognition system In Detail Machine
learning is becoming increasingly pervasive in the modern
data-driven world. It is used extensively across many fields such
as search engines, robotics, self-driving cars, and more. With this
book, you will learn how to perform various machine learning tasks
in different environments. We'll start by exploring a range of
real-life scenarios where machine learning can be used, and look at
various building blocks. Throughout the book, you'll use a wide
variety of machine learning algorithms to solve real-world problems
and use Python to implement these algorithms. You'll discover how
to deal with various types of data and explore the differences
between machine learning paradigms such as supervised and
unsupervised learning. We also cover a range of regression
techniques, classification algorithms, predictive modeling, data
visualization techniques, recommendation engines, and more with the
help of real-world examples. Style and approach You will explore
various real-life scenarios in this book where machine learning can
be used, and learn about different building blocks of machine
learning using independent recipes in the book.
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