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F# for Machine Learning Essentials Cover

F# for Machine Learning Essentials

Get up and running with machine learning with F# in a fun and functional way

Publisher:Packt Publishing Limited
Paid access
|Jul 2025
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Description

Get up and running with machine learning with F# in a fun and functional way

Key Features

  • Design algorithms in F# to tackle complex computing problems
  • Be a proficient F# data scientist using this simple-to-follow guide
  • Solve real-world, data-related problems with robust statistical models, built for a range of datasets

Book Description

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

Who this book is for

If you are a C# or an F# developer who now wants to explore the area of machine learning, then this book is for you. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage.

Table of Contents

  1. Introduction to ML
  2. Predicting Real Numbers using Linear Regression
  3. Classification Techniques
  4. Information Retrieval
  5. Recommendation Systems
  6. Sentiment Analysis
  7. Anomaly Detection
PDF ISBN: 978-1-78398-935-5
Publisher: Packt Publishing Limited
Copyright owner: © 2016 Packt Publishing Limited
Publication date: 2025
Language: English
Pages: 194
Related subjects:
Computer sciences,
Computer sciences, other

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