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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Design efficient machine learning systems that give you more accurate results
About This Book
- Gain an understanding of the machine learning design process
- Optimize machine learning systems for improved accuracy
- Understand common programming tools and techniques for machine learning
- Develop techniques and strategies for dealing with large amounts of data from a variety of sources
- Build models to solve unique tasks
Who This Book Is For
This book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts.
What You Will Learn
- Gain an understanding of the machine learning design process
- Optimize the error function of your machine learning system
- Understand the common programming patterns used in machine learning
- Discover optimizing techniques that will help you get the most from your data
- Find out how to design models uniquely suited to your task
In Detail
Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles.
There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Style and approach
This easy-to-follow, step-by-step guide covers the most important machine learning models and techniques from a design perspective.
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Seitenzahl: 301
Veröffentlichungsjahr: 2016
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Table of Contents
Designing Machine Learning Systems with Python
Designing Machine Learning Systems with Python
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: April 2016
Production reference: 1310316
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-78588-295-1
www.packtpub.com
Credits
Author
David Julian
Reviewer
Dr. Vahid Mirjalili
Commissioning Editor
Veena Pagare
Acquisition Editor
Tushar Gupta
Content Development Editor
Merint Thomas Mathew
Technical Editor
Abhishek R. Kotian
Copy Editor
Angad Singh
Project Coordinator
Suzanne Coutinho
Proofreader
Safis Editing
Indexer
Rekha Nair
Graphics
Disha Haria
Jason Monteiro
Production Coordinator
Aparna Bhagat
Cover Work
Aparna Bhagat
About the Author
David Julian is currently working on a machine learning project with Urban Ecological Systems Ltd and Blue Smart Farms (http://www.bluesmartfarms.com.au) to detect and predict insect infestation in greenhouse crops. He is currently collecting a labeled training set that includes images and environmental data (temperature, humidity, soil moisture, and pH), linking this data to observations of infestation (the target variable), and using it to train neural net models. The aim is to create a model that will reduce the need for direct observation, be able to anticipate insect outbreaks, and subsequently control conditions. There is a brief outline of the project at http://davejulian.net/projects/ues. David also works as a data analyst, I.T. consultant, and trainer.
I would like to thank Hogan Gleeson, James Fuller, Kali McLaughlin and Nadine Miller. This book would not have been possible without the great work of the open source machine learning community.
About the Reviewer
Dr. Vahid Mirjalili is a data scientist with a diverse background in engineering, mathematics, and computer science. With his specialty in data mining, he is very interested in predictive modeling and getting insights from data. Currently, he is working towards publishing a book on big data analysis, which covers a wide range of tools and techniques for analyzing massive data sets. Furthermore, as a Python developer, he likes to contribute to the open source community. He has developed Python packages for data clustering, such as PyClust. A collection of his tutorials and programs on data science can be found in his Github repository at http://github.com/mirjalil/DataScience. For more information, please visit his personal website at http://vahidmirjalili.com.
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Preface
Machine learning is one of the biggest trends that the world of computing has seen. Machine learning systems have a profound and exciting ability to provide important insights on an amazing variety of applications, from ground-breaking and lifesaving medical research to discovering fundamental physical aspects of our universe; from providing us with better, cleaner food to web analytics and economic modeling. In fact, there is hardly any area of our lives that is not touched by this technology in some way. Everyone wants to get into the field of machine learning, and in order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.
What this book covers
Chapter 1, Thinking in Machine Learning, gets you started with the basics of machine learning, and as the title says, it will help you think in the machine learning paradigm. You will learn the design principles and various models involved in machine learning.
Chapter 2, Tools and Techniques, explains that Python comes equipped with a large library of packages for machine learning tasks. This chapter will give you a flavor of some huge libraries. It will cover packages such as NumPy, SciPy, Matplotlib, and Scilit-learn.
Chapter 3, Turning Data into Information, explains that raw data can be in many different formats and can be of varying quantity and quality. Sometimes, we are overwhelmed by data, and sometimes we struggle to get every last drop of information from our data. For data to become information, it requires some meaningful structure. In this chapter, we will introduce some broad topics such as big data, data properties, data sources, and data processing and analysis.
Chapter 4, Models – Learning from Information, takes you through the logical models—where we explore a logical language and create a hypothesis space mapping, tree models – where we will find that they can be applied to a wide range of tasks and are both descriptive and easy to interpret; and rule models – where we discuss both ordered rule list- and unordered rule set-based models.
Chapter 5, Linear Models, introduces one of the most widely used models that forms the foundation of many advanced nonlinear techniques, such as support vector machines and neural networks. In this chapter, we will study some of the most commonly used techniques in machine learning. We will create hypothesis representations for linear and logistic regression.
Chapter 6, Neural Networks, introduces the powerful machine learning algorithm of artificial neural networks. We will see how these networks are a simplified model of neurons in the brain.
Chapter 7, Features – How Algorithms See the World, goes through the different types of feature—the Quantitative, Ordinal, and Categorical features. We will also learn the Structured and Transforming features in detail.
Chapter 8, Learning with Ensembles, explains the reason behind the motivation for creating machine learning ensembles, which comes from clear intuitions and is grounded in a rich theoretical history. The types of machine learning ensemble that can be created are as diverse as the models themselves, and the main considerations revolve around three things: how we divide our data, how we select the models, and the methods we use to combine their results.
Chapter 9, Design Strategies and Case Studies, looks at some design strategies to ensure your machine learning applications perform optimally. We will learn model selection and parameter tuning techniques, and apply them to several case studies.
What you need for this book
All you need is an inclination to learn machine learning and the Python V3 software, which you can download from https://www.python.org/downloads/.
Who this book is for
This book is for data scientists, scientists, or just the curious. You will need to know some linear algebra and some Python. You will need to have some basic knowledge of machine learning concepts.
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Chapter 1. Thinking in Machine Learning
Machine learning systems have a profound and exciting ability to provide important insights to an amazing variety of applications; from groundbreaking and life-saving medical research, to discovering fundamental physical aspects of our universe. From providing us with better, cleaner food, to web analytics and economic modeling. In fact, there are hardly any areas of our lives that have not been touched by this technology in some way. With an expanding Internet of Things, there is a staggering amount of data being generated, and it is clear that intelligent systems are changing societies in quite dramatic ways. With open source tools, such as those provided by Python and its libraries, and the increasing open source knowledge base represented by the Web, it is relatively easy and cheap to learn and apply this technology in new and exciting ways. In this chapter, we will cover the following topics:
The human interface
For those of you old enough, or unfortunate enough, to have used early versions of the Microsoft office suite, you will probably remember the Mr Clippy office assistant. This feature, first introduced in Office 97, popped up uninvited from the bottom right-hand side of your computer screen every time you typed the word 'Dear' at the beginning of a document, with the prompt "it looks like you are writing a letter, would you like help with that?".
Mr Clippy, turned on by default in early versions of Office, was almost universally derided by users of the software and could go down in history as one of machine learning's first big fails.
So, why was the cheery Mr Clippy so hated? Clearly the folks at Microsoft, at the forefront of consumer software development, were not stupid, and the idea that an automated assistant could help with day to day office tasks is not necessarily a bad idea. Indeed, later incarnations of automated assistants, the best ones at least, operate seamlessly in the background and provide a demonstrable increase in work efficiency. Consider predictive text. There are many examples, some very funny, of where predictive text has gone spectacularly wrong, but in the majority of cases where it doesn't fail, it goes unnoticed. It just becomes part of our normal work flow.
At this point, we need a distinction between error and failure. Mr Clippy failed because it was obtrusive and poorly designed, not necessarily because it was in error; that is, it could make the right suggestion, but chances are you already know that you are writing a letter. Predictive text has a high error rate, that is, it often gets the prediction wrong, but it does not fail largely because of the way it is designed to fail: unobtrusively.
The design of any system that has a tightly coupled human interface, to use systems engineering speak, is difficult. Human behavior, like the natural world in general, is not something we can always predict. Expression recognition systems, natural language processing, and gesture recognition technology, amongst other things, all open up new ways of human-machine interaction, and this has important applications for the machine learning specialist.
Whenever we are designing a system that requires human input, we need to anticipate the possible ways, not just the intended ways, a human will interact with the system. In essence, what we are trying to do with these systems is to instil in them some understanding of the broad panorama of human experience.
In the first years of the web, search engines used a simple system based on the number of times search terms appeared in articles. Web developers soon began gaming the system by increasing the number of key search terms. Clearly, this would lead to a keyword arms race and result in a very boring web. The page rank system measuring the number of quality inbound links was designed to provide a more accurate search result. Now, of course, modern search engines use more sophisticated and secret algorithms.
What is also important for ML designers is the ever increasing amount of data that is being generated. This presents several challenges, most notably its sheer vastness. However, the power of algorithms in extracting knowledge and insights that would not have been possible with smaller data sets is massive. So, many human interactions are now digitized, and we are only just beginning to understand and explore the many ways in which this data can be used.
As a curious example, consider the study The expression of emotion in 20th century books (Acerbi et al, 2013). Though strictly more of a data analysis study, rather than machine learning, it is illustrative for several reasons. Its purpose was to chart the emotional content, in terms of a mood score, of text extracted from books of the 20th century. With access to a large volume of digitized text through the project Gutenberg digital library, WordNet (http://wordnet.princeton.edu/wordnet/), and Google's Ngram database (books.google.com/ngrams), the authors of this study were able to map cultural change over the 20th century as reflected in the literature of the time. They did this by mapping trends in the usage of the mood words.
For this study, the authors labeled each word (a 1gram) and associated it with a mood score and the year it was published. We can see that emotion words, such as joy, sadness, fear, and so forth, can be scored according to the positive or negative mood they evoke. The mood score was obtained from WordNet (wordnet.princeton.edu). WordNet assigns an affect score to each mood word. Finally, the authors simply counted the occurrences of each mood word:
Here, ci is the count of a particular mood word, n is the total count of mood words (not all words, just words with a mood score), and Cthe is the count of the word the in the text. This normalizes the sum to take into account that some years more books were written (or digitized). Also, since many later books tend to contain more technical language, the word the was used to normalize rather than get the total word count. This gives a more accurate representation of emotion over a long time period in prose text. Finally, the score is normalized according to a normal distribution, Mz, by subtracting the mean and dividing by the standard deviation.
This figure is taken from The expression of Emotions in 20th Century Books, (Alberto Acerbi, Vasileios Lampos, Phillip Garnett, R. Alexander Bentley) PLOS.
Here we can see one of the graphs generated by this study. It shows the joy-sadness score for books written in this period, and clearly shows a negative trend associated with the period of World War II.
This study is interesting for several reasons. Firstly, it is an example of data-driven science, where previously considered soft sciences, such as sociology and anthropology, are given a solid empirical footing. Despite some pretty impressive results, this study was relatively easy to implement. This is mainly because most of the hard work had already been done by WordNet and Google. This highlights how using data resources that are freely available on the Internet, and software tools such as the Python's data and machine learning packages, anyone with the data skills and motivation can build on this work.
