F# for Machine Learning Essentials

Learn machine learning techniques in a fun and functional Way

About This Book

• Solve real-world problems using code written in F# and other industry-standard APIs
• Learn how many different types of machine learning algorithms there are and how they work
• Along the way, learn how to use several industry-standard frameworks (such as Accord.NET, WekaSharp, and Math.NET) along with F# to solve problems using machine learning algorithms.

Who This Book Is For

This book is for intermediate C#/ Beginner F# programmers. No knowledge of machine learning is assumed.

What You Will Learn

• Categorize a real-world problem as a machine learning problem of a given kind; for example, supervised / clustering etc
• Learn Math.NET to perform mathematical operations on matrices and vectors using F# in a functional declarative way.
• Know how e-commerce sites such as amazon.com/ flipkart.com and so on recommend an item for you and also how you can use a recommender system for your own problem domain.
• Figure out techniques to find symmetric and asymmetric similarity measures amongst several entities and learn about how to use them
• Build a set of classification systems using Accord.NET/ Weka/ F#
• Gain an understanding of MBrace to run machine learning jobs on the Cloud for better performance

In Detail

F# is a mature, open source, cross-platform, functional-first programming language that empowers users and organizations to tackle complex computing problems with simple, maintainable, and robust code. It is used in a wide range of application areas and is supported by both the open community and industry-leading companies providing professional tools.

This book aims to democratize machine learning concepts for all.NET developers. It starts with a simple introduction to several types of machine learning and how the various types of learning algorithm work. The major section of the title covers the subject of "Supervised Learning" in multiple chapters. Finally the book aims to bring the latest design paradigm "cloud computing" to the table. It will introduce readers to MBrace, a framework where users get to run several computing expressions over Microsoft Azure clouds; this is very important for machine learning because many of the machine learning algorithms is computationally very intense.

Sudipta Mukherjee

Sudipta Mukherjee was born in Kolkata and migrated to Bangalore. He is an electronics engineer by education and a computer engineer/scientist by profession and passion. He graduated in 2004 with a degree in electronics and communication engineering. He has a keen interest in data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning at large. His first book on Data Structure using C has been received quite well. Parts of the book can be read on Google Books at http://goo.gl/pttSh. The book was also translated into simplified Chinese, available from Amazon.cn at http://goo.gl/lc536. This is Sudipta's second book with Packt Publishing. His first book, .NET 4.0 Generics (http://goo.gl/MN18ce), was also received very well. During the last few years, he has been hooked to the functional programming style. His book on functional programming, Thinking in LINQ (http://goo.gl/hm0lNF), was released last year. Last year, he also gave a talk at @FuConf based on his LINQ book (https://goo.gl/umdxIX). He lives in Bangalore with his wife and son. Sudipta can be reached via e-mail at sudipto80@yahoo.com and via Twitter at @samthecoder.