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Key benefits

*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

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

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Amazon Customer Dec 10, 2016

The cookbook is excellent. Focused and relevant to the needs of the machine learnig community. The author has communicated with clarity for the individual who would like to learn the practical aspects of implementing learning algorithms of today and for the future. Excellent work, uptodate and very relevant for the applications of the day!. Every algorithm works and is applicable easily.

Amazon Verified review

Spoorthi V. Jul 28, 2016

I'm relatively new to Python and I would like to say that this book is very friendly to Python beginners. The projects were easy to understand and the code is explained step by step. It was interesting to learn how to work with different types of data like images, text, and audio. I would definitely recommend this book to people who want to get started with machine learning in Python.

Nari Aug 08, 2016

I would say this book is ideal for anyone who knows some Machine Learning basics and has experience with Python, but it's also a great book for beginners who want to learn about practical ML problems. I've taken Andrew Ng’s Stanford Machine Learning courses in the past, and converting the theories into code isn't always intuitive. However, this book teaches you how to implement those algorithms into code, with lots of practical problems and easy-to-understand example code. Also, the additional graphs and images helped me visualize the concepts. Highly recommended!

Rajesh Ranjan Dec 04, 2018

👍🏻

P. Sebastien May 28, 2017

I didnot fall in love at all with this book. many recipes, but treated very fast. I found a couple of tips, but other books of same publisher are better i brlive

Python Machine Learning Cookbook: 100 recipes that teach you how to perform various machine learning tasks in the real world

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