Mueller Andreas C. - Introduction to Machine Learning with Python
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Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.Youll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.Table of Contents:
Introduction.
Supervised Learning.
Unsupervised Learning and Preprocessing.
Summary of scikit-learn methods and usage.
Representing Data and Engineering Features.
Model evaluation and improvement.
Algorithm Chains and Pipelines.
Working with Text Data.
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by Andreas C. Mueller and Sarah Guido
Copyright 2016 Sarah Guido, Andreas Mueller. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles ( http://safaribooksonline.com ). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- Illustrator: Rebecca Demarest
- June 2016: First Edition
- 2016-06-09: First Early Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491917213 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Introduction to Machine Learning with Python, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
While the publisher and the author(s) have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author(s) disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-491-91721-3
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Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence and computer science, which is also known as predictive analytics or statistical learning. The application of machine learning methods has in recent years become ubiquitous in everyday life. From automatic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing your friends in your photos, many modern websites and devices have machine learning algorithms at their core.
When you look at at complex websites like Facebook, Amazon or Netflix, it is very likely that every part of the website you are looking at contains multiple machine learning models.
Outside of commercial applications, machine learning has had a tremendous influence on the way data driven research is done today. The tools introduced in this book have been applied to diverse scientific questions such as understanding stars, finding distant planets, analyzing DNA sequences, and providing personalized cancer treatments.
Your application doesnt need to be as large-scale or world-changing as these examples in order to benefit from machine learning. In this chapter, we will explain why machine learning became so popular, and dicuss what kind of problem can be solved using machine learning. Then, we will show you how to build your first machine learning model, introducing important concepts on the way.
In the early days of intelligent applications, many systems used hand-coded rules of if and else decisions to process data or adjust to user input. Think of a spam filter whose job is to move an email to a spam folder. You could make up a black-list of words that would result in an email marked as spam. This would be an example of using an expert designed rule system to design an intelligent application. Designing kind of manual design of decision rules is feasible for some applications, in particular for those applications in which humans have a good understanding of how a decision should be made. However, using hand-coded rules to make decisions has two major disadvantages:
The logic required to make a decision is specific to a single domain and task. Changing the task even slightly might require a rewrite of the whole system.
Designing rules requires a deep understanding of how a decision should be made by a human expert.
One example of where this hand-coded approach will fail is in detecting faces in images. Today every smart phone can detect a face in an image. However, face detection was an unsolved problem until as recent as 2001. The main problem is that the way in which pixels (which make up an image in a computer) are perceived by the computer is very different from how humans perceive a face. This difference in representation makes it basically impossible for a human to come up with a good set of rules to describe what constitutes a face in a digital image.
Using machine learning, however, simply presenting a program with a large collection of images of faces is enough for an algorithm to determine what characteristics are needed to identify a face.
The most successful kind of machine learning algorithms are those that automate a decision making processes by generalizing from known examples. In this setting, which is known as a supervised learning setting, the user provides the algorithm with pairs of inputs and desired outputs, and the algorithm finds a way to produce the desired output given an input.
In particular, the algorithm is able to create an output for an input it has never seen before without any help from a human.
Going back to our example of spam classification, using machine learning, the user provides the algorithm a large number of emails (which are the input), together with the information about whether any of these emails are spam (which is the desired output). Given a new email, the algorithm will then produce a prediction as to whether or not the new email is spam.
Machine learning algorithms that learn from input-output pairs are called supervised learning algorithms because a teacher provides supervision to the algorithm in the form of the desired outputs for each example that they learn from.
While creating a dataset of inputs and outputs is often a laborious manual process, supervised learning algorithms are well-understood and their performance is easy to measure. If your application can be formulated as a supervised learning problem, and you are able to create a dataset that includes the desired outcome, machine learning will likely be able to solve your problem.
Examples of supervised machine learning tasks include:
- Identifying the ZIP code from handwritten digits on an envelope. Here the input is a scan of the handwriting, and the desired output is the actual digits in the zip code. To create a data set for building a machine learning model, you need to collect many envelopes. Then you can read the zip codes yourself and store the digits as your desired outcomes.
- Determining whether or not a tumor is benign based on a medical image. Here the input is the image, and the output is whether or not the tumor is benign. To create a data set for building a model, you need a database of medical images. You also need an expert opinion, so a doctor needs to look at all of the images and decide which tumors are benign and which are not.
- Detecting fraudulent activity in credit card transactions.
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