Gavin Hackeling - Mastering Machine Learning with scikit-learn

Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's degree from the University of North Carolina.

To Hallie, for her support, and Zipper, without whose contributions this book would have been completed in half the time.

Fahad Arshad completed his PhD at Purdue University in the Department of Electrical and Computer Engineering. His research interests focus on developing algorithms for software testing, error detection, and failure diagnosis in distributed systems. He is particularly interested in data-driven analysis of computer systems. His work has appeared at top dependability conferencesDSN, ISSRE, ICAC, Middleware, and SRDSand he has been awarded grants to attend DSN, ICAC, and ICNP. Fahad has also been an active contributor to security research while working as a cybersecurity engineer at NEEScomm IT. He has recently taken on a position as a systems engineer in the industry.

Sarah Guido is a data scientist at Reonomy, where she's helping build disruptive technology in the commercial real estate industry. She loves Python, machine learning, and the startup world. She is an accomplished conference speaker and an O'Reilly Media author, and is very involved in the Python community. Prior to joining Reonomy, Sarah earned a Master's degree from the University of Michigan School of Information.

Mikhail Korobov is a software developer at ScrapingHub Inc., where he works on web scraping, information extraction, natural language processing, machine learning, and web development tasks. He is an NLTK team member, Scrapy team member, and an author or contributor to many other open source projects.

I'd like to thank my wife, Aleksandra, for her support and patience and for the cookies.

Aman Madaan is currently pursuing his Master's in Computer Science and Engineering. His interests span across machine learning, information extraction, natural language processing, and distributed computing. More details about his skills, interests, and experience can be found at http://www.amanmadaan.in.

www.PacktPub.com

Support files, eBooks, discount offers, and more

You might want to visit www.PacktPub.com for support files and downloads related to your book.

Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at > for more details.

At www.PacktPub.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.

http://PacktLib.PacktPub.com

Do you need instant solutions to your IT questions? PacktLib is Packt's online digital book library. Here, you can access, read, and search across Packt's entire library of books.

Why subscribe?

• Fully searchable across every book published by Packt
• Copy and paste, print, and bookmark content
• On demand and accessible via web browser

Free access for Packt account holders

If you have an account with Packt at www.PacktPub.com, you can use this to access PacktLib today and view nine entirely free books. Simply use your login credentials for immediate access.

Preface

Recent years have seen the rise of machine learning, the study of software that learns from experience. While machine learning is a new discipline, it has found many applications. We rely on some of these applications daily; in some cases, their successes have already rendered them mundane. Many other applications have only recently been conceived, and hint at machine learning's potential.

In this book, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly applied to, and learn to measure the performance of machine learning systems. We will work with a popular library for the Python programming language called scikit-learn, which has assembled excellent implementations of many machine learning models and algorithms under a simple yet versatile API.

This book is motivated by two goals:

• Its content should be accessible. The book only assumes familiarity with basic programming and math.
• Its content should be practical. This book offers hands-on examples that readers can adapt to problems in the real world.

What this book covers

, The Fundamentals of Machine Learning , defines machine learning as the study and design of programs that improve their performance of a task by learning from experience. This definition guides the other chapters; in each chapter, we will examine a machine learning model, apply it to a task, and measure its performance.

, Linear Regression , discusses linear regression, a model that relates explanatory variables and model parameters to a continuous response variable. You will learn about cost functions, and use the normal equation to find the parameter values that produce the optimal model.