Book Image

F# for Machine Learning Essentials

By : Sudipta Mukherjee
Book Image

F# for Machine Learning Essentials

By: Sudipta Mukherjee

Overview of this book

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.
Table of Contents (16 chapters)
F# for Machine Learning Essentials
About the Author
About the Reviewers


In this chapter, you have learnt about several different types of machine learning techniques and their possible usages. Try to spot probable machine learning algorithms that might be deployed deep inside some applications. Following are some examples of machine learning. Your mailbox is curated by an automatic spam protector and it learns every time you move an e-mail from your inbox to the spam folder. This is an example of a supervised classification algorithm. When you apply for a health insurance, then based on several parameters, they (the insurance company) try to fit your data and predict what premium you might have to pay. This is an example of linear regression. Sometimes when people buy baby diapers at supermarkets, they get a discount coupon for buying beer. Sounds crazy, right! But the machine learning algorithm figured out that people who buy the diapers buy beer too. So they want to provoke the users to buy more. There is lot of buzz right now about predictive analytics. It is nothing but predicting an event in the future by associating a probability score. For example, figuring out how long will a shopper take to return to the store for her next purchase. These data are extracted from the visit patterns. That's unsupervised learning working in the background.

Sometimes one simple algorithm doesn't provide the needed accuracy. So then several methods are used and a special class of algorithm, known as Ensemble method, is used to club the individual results. In loose terms, it kind of resonates with the phrase "crowd-smart". You will learn about some ensemble methods in a later chapter.

I want to wrap up this chapter with the following tip. When you have a problem that you want to solve and you think machine learning can help, follow the following steps. Break the problem into smaller chunks and then try to locate a class of machine learning problem domain for this smaller problem. Then find the best method in that class to solve. Iterate over and over until your error rates are within permissible limits. And then wrap it in a nice application/user interface.