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


Sentiment Analysis is a very active area of research right now. There are several aspects about this task that makes it very hard. In this chapter, the basic techniques have been discussed. However, sometimes the positive sentiment can be negatively perceived. For example, if someone is checking the public sentiment for a political figure then any statement with a positive sentiment towards an opponent of the political figure in question should actually be treated as a negative sentiment. Also, identifying sarcasm in phrases can be very challenging and that can lead to an increase in a false positive rate. Moreover, identifying the target of the sentiment automatically can be quite challenging. For example, consider the following statement the food was great but the décor was old-fashioned. If this statement appears for a website that rates restaurants, then identifying the sentiment towards food as positive but that for décor (another possible feature in the restaurant entity) as...