Combs - Python machine learning blueprints intuitive data projects you can relate to: an approachable guide to applying advanced machine learning methods to everyday problems
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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: July 2016
Production reference: 1270716
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78439-475-2
www.packtpub.com
Author Alexander T. Combs | Copy Editor Priyanka Ravi |
Reviewer Kushal Khandelwal | Project Coordinator Suzanne Coutinho |
Commissioning Editor Kartikey Pandey | Proofreader Safis Editing |
Acquisition Editor Vivek Anantharaman Manish Nainani | Indexer Rekha Nair |
Content Development Editor Merint Thomas Mathew | Production Coordinator Melwyn Dsa |
Technical Editor Abhishek R. Kotian | Cover Work Melwyn Dsa |
Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.
Writing a book is truly a massive undertaking that would not be possible without the support of others. I would like to thank my family for their love and encouragement and Jocelyn for her patience and understanding. I owe all of you tremendously.
Kushal Khandelwal is a data scientist and a full-stack developer. His interests include building scalable machine learning and image processing software applications. He is adept at coding in Python and contributes actively to various open source projects. He is currently serving as the Head of technology at Truce.in, a farmer-centric start-up where he is building scalable web applications to assist farmers.
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Machine learning is rapidly becoming a fixture in our data-driven world. It is relied upon in fields as diverse as robotics and medicine to retail and publishing. In this book, you will learn how to build real-world machine learning applications step by step.
Working through easy-to-understand projects, you will learn how to process various types of data and how and when to apply different machine learning techniques such as supervised or unsupervised learning.
Each of the projects in this book provides educational as well as practical value. For example, you'll learn how to use clustering techniques to find bargain airfares and how to use linear regression to find a cheap apartment. This book will teach you how to use machine learning to collect, analyze, and act on massive quantities of data in an approachable, no-nonsense manner.
, The Python Machine Learning Ecosystem , delves into Python, which has a deep and active developer community, and many of these developers come from the scientific community as well. This has provided Python with a rich array of libraries for scientific computing. In this chapter, we will discuss the features of these key libraries and how to prepare your environment to best utilize them.
, Build an App to Find Underpriced Apartments , guides us to build our first machine learning application, and we begin with a minimal but practical example: building an application to identify underpriced apartments. By the end of this chapter, we will create an application that will make finding the right apartment a bit easier.
, Build an App to Find Cheap Airfares , demonstrates how to build an application that continually monitors fare pricing. Once an anomalous price appears, our app will generate an alert that we can quickly act on.
, Forecast the IPO Market using Logistic Regression , shows how we can use machine learning to decide which IPOs are worth a closer look and which ones we may want to skip.
, Create a Custom Newsfeed , covers how to build a system that understands your taste in news and will send you a personally tailored newsletter each day.
, Predict whether Your Content Will Go Viral , examines some of the most shared content and attempts to find the common elements that differentiate it from the content that people are less willing to share.
, Forecast the Stock Market with Machine Learning , discusses how to build and test a trading strategy. There are countless pitfalls to avoid when trying to devise your own system, and it is quite nearly an impossible task. However, it can be a lot of fun, and sometimes, it can even be profitable.
, Build an Image Similarity Engine , helps you construct an advanced, image-based, deep learning application. We will also cover deep learning algorithms to understand why they are so important and why there is such a hype surrounding them.
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