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

Predicting a traffic jam using a decision tree: a case study

When I go home from the office, I face a traffic jam, as shown in the following image, as many other commuters in Bangalore do almost every day.

I thought if I could only predict a traffic jam, I would reach home early to play with my son. I observed that if it rains and if it is a weekday and if it is past 4:30 PM in the dial, a traffic jam is highly likely.

So I thought I could use a decision tree to predict whether there will be a traffic jam today or not. In the following example, I used a toy dataset that I fabricated. In the real settings, the data has to be filled following a month or few months' observations, because anomalies do exist. Sometimes, even if it rains and even if it is a weekday, there is no traffic jam. The reason may be that there is a cricket match that people want to watch and they have taken a day off work or are working from home. However, these sorts of situations are not common and shouldn't be considered...