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Python Machine Learning Cookbook: 100 recipes that teach you how to perform various machine learning tasks in the real world,Used
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100 recipes that teach you how to perform various machine learning tasks in the real worldKey Features Understand which algorithms to use in a given context with the help of this exciting recipebased 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 techniquesBook DescriptionMachine learning is becoming increasingly pervasive in the modern datadriven world. It is used extensively across many fields such as search engines, robotics, selfdriving 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 reallife 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 realworld 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 realworld examples.What you will learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to realworld 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 systemWho this book is for:This book is for Python programmers who are looking to use machinelearning algorithms to create realworld applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.
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