Ciaburro Giuseppe - Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets
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Copyright 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: June 2016
Production reference: 1160616
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78646-447-7
www.packtpub.com
Author
Prateek Joshi
Reviewer
Dr. Vahid Mirjalili
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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.
I would like to thank the reviewers of this book for their valuable comments and suggestions. I would also like to thank the wonderful team at Packt Publishing for publishing the book and helping me all along. Finally, I would like to thank my family for supporting me through everything.
Dr. Vahid Mirjalili is a software engineer and data scientist with a diverse background in engineering, mathematics, and computer science. Currently, he is working toward his graduate degree in Computer Science at Michigan State University. He teaches Python programming as well as computing concepts and the fundamentals of data analysis with Excel and databases using Microsoft Access. With his specialty in data mining, he is keenly interested in predictive modeling and getting insights from data. He is also a Python developer, and he likes to contribute to the open source community. Furthermore, he is also focused in making tutorials for different directions of data science and computer algorithms, which you can find at his GitHub repository, http://github.com/mirjalil/DataScience.
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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 so on. In this book, you will explore various real-life scenarios where you can use machine learning. You will understand what algorithms you should use in a given context using this exciting recipe-based guide.
This book starts by talking about various realms in machine learning followed by practical examples. We then move on to discuss more complex algorithms, such as Support Vector Machines, Extremely Random Forests, Hidden Markov Models, Conditional Random Fields, Deep Neural Networks, and so on. This book is for Python programmers looking to use machine learning algorithms to create real-world applications. This book is friendly to Python beginners but familiarity with Python programming will certainly be helpful to play around with the code. It is also useful to experienced Python programmers who are looking to implement machine learning techniques.
You will learn how to make informed decisions about the types of algorithm that you need to use and how to implement these algorithms to get the best possible results. If you get stuck while making sense of images, text, speech, or some other form of data, this guide on applying machine learning techniques to each of these will definitely come to your rescue!
, The Realm of Supervised Learning , covers various supervised-learning techniques for regression. We will learn how to analyze bike-sharing patterns and predict housing prices.
, Constructing a Classifier , covers various supervised-learning techniques for data classification. We will learn how to estimate the income brackets and evaluate a car based on its characteristics.
, Predictive Modeling , discusses predictive-modeling techniques using Support Vector Machines. We will learn how to apply these techniques to predict events occurring in buildings and traffic on the roads near sports stadiums.
, Clustering with Unsupervised Learning , explains unsupervised learning algorithms, including k-means and Mean Shift clustering. We will learn how to apply these algorithms to stock market data and customer segmentation.
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