PACK | Python Machine Learning Cookbook (2016 EN)

Author: Prateek Joshi
Full Title: Python Machine Learning Cookbook
Publisher: Packt Publishing - ebooks Account (September 6, 2016)
Year: 2016
ISBN-13: 9781786464477 (978-1-78646-447-7)
ISBN-10: 1786464470
Pages: 304
Language: English
Genre: Databases & Big Data
File type: AZW3, PDF (True, but nonnative Cover)
Quality: 7/10 (AZW3), 9/10 (PDF)
Price: $59.99


100 recipes that teach you how to perform various machine learning tasks in the real world.

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.


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

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