Python Machine Learning Cookbook

Key Features

• 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

Book Description

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

About the Author

Prateek Joshi is an Artificial Intelligence researcher and a published author. He has over eight years of experience in this field with a primary focus on content-based analysis and deep learning. He has written two books on Computer Vision and Machine Learning. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences.

People from all over the world visit his blog, and he has received more than a million page views from over 200 countries. He has been featured as a guest author in prominent tech magazines. He enjoys blogging about topics, such as Artificial Intelligence, Python programming, abstract mathematics, and cryptography. You can visit his blog at www.prateekvjoshi.com.

He has won many hackathons utilizing a wide variety of technologies. He is an avid coder who is passionate about building game-changing products. He graduated from University of Southern California, and he has worked at companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. You can learn more about him on his personal website at www.prateekj.com.

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