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F# for Machine Learning Essentials
F# for Machine Learning Essentials

F# for Machine Learning Essentials: Get up and running with machine learning with F# in a fun and functional way

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Profile Icon Sudipta Mukherjee
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AU$24.99 per month
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
Paperback Feb 2016 194 pages 1st Edition
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AU$43.99 AU$48.99
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Arrow left icon
Profile Icon Sudipta Mukherjee
Arrow right icon
AU$24.99 per month
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
Paperback Feb 2016 194 pages 1st Edition
eBook
AU$43.99 AU$48.99
Paperback
AU$60.99
Subscription
Free Trial
Renews at AU$24.99p/m
eBook
AU$43.99 AU$48.99
Paperback
AU$60.99
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Free Trial
Renews at AU$24.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

F# for Machine Learning Essentials

Chapter 2. Linear Regression

"Honey! How much will gasoline cost next year?"

Linear regression is a technique to predict the value of a feature/attribute in a continuous range. It is similar to classification in a way that both types of algorithms solve a similar problem. But classification yields a discrete value as the tag while regression tries to predict a real value. In this chapter, you will learn how linear regression works and how it can be used in real-life settings.

Objective

After reading this chapter, you will be able to understand how several linear regression algorithms work and how to tune your linear regression model. You will also learn to use some parts of Math.NET and Accord.NET, which make implementing some of the linear regression algorithms simple. Along the way, you will also learn how to use FsPlot to plot various charts. All source code is made available at https://gist.github.com/sudipto80/3b99f6bbe9b21b76386d.

Different types of linear regression algorithms

Based on the approach used and the number of input parameters, there are several types of linear regression algorithms to determine the real value of the target variable. In this chapter, you will learn how to implement the following algorithms using F#:

  • Simple Least Square Linear Regression
  • Multiple Linear Regression
  • Weighted Linear regression
  • Ridge Regression
  • Multivariate Multiple Linear Regression

These algorithms will be implemented using a robust industry standard open source .NET mathematics API called Math.NET. Math.NET has an F# friendly wrapper.

APIs used

In this chapter, you will learn how to use the preceding APIs to solve problems using several linear regression methods and plot the result.

APIs used

FsPlot is a charting library for F# to generate charts using industry standard JavaScript charting APIs, such as HighCharts. FsPlot provides a nice interface to generate several combination charts, which is very useful when trying to understand the linear regression model. You can find more details about the API at its homepage at https://github.com/TahaHachana/FsPlot.

Math.NET Numerics for F# 3.7.0

Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, in engineering, and in everyday use. It supports F# 3.0 on .Net 4.0, .Net 3.5, and Mono on Windows, Linux, and Mac; Silverlight 5 and Windows 8 with PCL portable profile 47; Android/iOS with Xamarin.

Math.NET Numerics for F# 3.7.0

You can get the API from the NuGet page at https://www.nuget.org/packages/MathNet.Numerics.FSharp/....

The basics of matrices and vectors (a short and sweet refresher)

Using Math.Net numerics, you can create matrices and vectors easily. The following section shows how. However, before you can create the vector or the matrix using Math.NET API, you have to reference the library properly. The examples in this chapter run using the F# script.

You have to write the following code at the beginning of the file and then run these in the F# interactive:

The basics of matrices and vectors (a short and sweet refresher)

Creating a vector

You can create a vector as follows:

Creating a vector

The vector values must always be float as per Math.NET. Once you run this in the F# interactive, it will create the following output:

Creating a vector

Creating a matrix

A matrix can be created in several ways using the Math.NET package. In the examples in this chapter, you will see the following ways most often:

  • Creating a matrix by hand: A matrix can be created manually using the Math.Net F# package, as follows:
    Creating a matrix

    Once you run this, you will get the following in the F# interactive:

    Creating a matrix
  • Creating a matrix from a list of rows...
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Key benefits

  • 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

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.

Who is this book 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.

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

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Publication date : Feb 25, 2016
Length: 194 pages
Edition : 1st
Language : English
ISBN-13 : 9781783989348
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Publication date : Feb 25, 2016
Length: 194 pages
Edition : 1st
Language : English
ISBN-13 : 9781783989348
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Frequently bought together


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Learning F# Functional Data Structures and Algorithms
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F# for Machine Learning Essentials
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Mastering F#
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Total AU$ 189.97 Stars icon

Table of Contents

8 Chapters
1. Introduction to Machine Learning Chevron down icon Chevron up icon
2. Linear Regression Chevron down icon Chevron up icon
3. Classification Techniques Chevron down icon Chevron up icon
4. Information Retrieval Chevron down icon Chevron up icon
5. Collaborative Filtering Chevron down icon Chevron up icon
6. Sentiment Analysis Chevron down icon Chevron up icon
7. Anomaly Detection Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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1 star 0%
Life long learner Jan 24, 2017
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There is no source code to download either thru the publisher or the link that the author provided. What code is listed in the book is fuzzy and not the same quality of print as the rest of the book.
Amazon Verified review Amazon
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