F# for Machine Learning is published by Packt Publishing in February 2016. This book has 194 pages in English, ISBN-13 978-1783989348.
The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs.
If you want to learn how to use F# to build machine learning systems, then this is the book you want.
Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.
What you will learn
- Use F# to find patterns through raw data
- Build a set of classification systems using Accord.NET, Weka, and F#
- Run machine learning jobs on the Cloud with MBrace
- Perform mathematical operations on matrices and vectors using Math.NET
- Use a recommender system for your own problem domain
- Identify tourist spots across the globe using inputs from the user with decision tree algorithms