Anglický jazyk

Python Machine Learning Cookbook

Na objednávku

67.05 €

bežná cena: 74.50 €

O knihe

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