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  • Book Overview & Buying F# for Machine Learning Essentials
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F# for Machine Learning Essentials

F# for Machine Learning Essentials

By : Sudipta Mukherjee
2 (1)
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F# for Machine Learning Essentials

F# for Machine Learning Essentials

2 (1)
By: Sudipta Mukherjee
Buy this Book

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 (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to Machine Learning
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1. Introduction to Machine Learning
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Objective
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Why use F#?
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Unsupervised learning
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Machine learning frameworks
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Machine learning for fun and profit
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Recognizing handwritten digits – your "Hello World" ML program
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Summary
2
2. Linear Regression
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2. Linear Regression
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Objective
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Different types of linear regression algorithms
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APIs used
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The basics of matrices and vectors (a short and sweet refresher)
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QR decomposition of a matrix
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Linear regression method of least square
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Finding linear regression coefficients using F#
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Finding the linear regression coefficients using Math.NET
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Putting it together with Math.NET and FsPlot
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Multiple linear regression
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Multiple linear regression and variations using Math.NET
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Weighted linear regression
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Plotting the result of multiple linear regression
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Ridge regression
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Multivariate multiple linear regression
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Feature scaling
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Summary
3
3. Classification Techniques
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3. Classification Techniques
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Objective
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Different classification algorithms you will learn
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Some interesting things you can do
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Understanding logistic regression
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Multiclass classification using logistic regression
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Multiclass classification using decision trees
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Predicting a traffic jam using a decision tree: a case study
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Challenge yourself!
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Summary
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4. Information Retrieval
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4. Information Retrieval
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Objective
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Different IR algorithms you will learn
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What interesting things can you do?
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Information retrieval using tf-idf
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5. Collaborative Filtering
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5. Collaborative Filtering
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Objective
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Different classification algorithms you will learn
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Vocabulary of collaborative filtering
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Baseline predictors
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Item-item collaborative filtering
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Top-N recommendations
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Evaluating recommendations
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Ranking accuracy metrics
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Working with real movie review data (Movie Lens)
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Summary
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6. Sentiment Analysis
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6. Sentiment Analysis
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Objective
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What you will learn
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A baseline algorithm for SA using SentiWordNet lexicons
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Handling negations
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Identifying praise or criticism with sentiment orientation
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Pointwise Mutual Information
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Using SO-PMI to find sentiment analysis
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Summary
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7. Anomaly Detection
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7. Anomaly Detection
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Objective
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Detecting point anomalies using IQR (Interquartile Range)
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Detecting point anomalies using Grubb's test
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Grubb's test for multivariate data using Mahalanobis distance
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Chi-squared statistic to determine anomalies
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Detecting anomalies using density estimation
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Strategy to convert a collective anomaly to a point anomaly problem
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Dealing with categorical data in collective anomalies
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Summary
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Index
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Index

Understanding logistic regression

Unlike linear regression which is used to predict the real values of a real entity, logistic regression is used to predict the class or tag of an unseen entry. Logistic regression's output is either a 0 or a 1 depicting the predicted class of the unseen entry. Logistic regression uses a smooth curve whose values range from 0 to 1 for all the values of the independent variable.

Sigmoid function (also called logistic function) is one option for this function. This is defined by the following formula:

The sigmoid function chart

The following chart is generated by the code snippet using FsPlot:

You need to install Chrome to get the chart rendered.

So you see that the function value approaches 1 as the value of X approaches infinity, and it approaches 0 as the value of approaches negative infinity. So for any given value of , you can determine the class if you set your threshold at 0.5. In other words, you can say that if for a given value of the value of the sigmoid...

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Programming languages
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F# for Machine Learning Essentials
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