Joshi P. - Python Machine Learning Cookbook [2016, PDF, ENG] torrent

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Python Machine Learning Cookbook

Год издания: 2016
Автор: Joshi P.

Издательство: Packt Publishing
ISBN: 9781786464477
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 304

Описание: 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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Table of Contents

1: The Realm of Supervised Learning
2: Constructing a Classifier
3: Predictive Modeling
4: Clustering with Unsupervised Learning
5: Building Recommendation Engines
6: Analyzing Text Data
7: Speech Recognition
8: Dissecting Time Series and Sequential Data
9: Image Content Analysis
10: Biometric Face Recognition
11: Deep Neural Networks
12: Visualizing Data
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