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.