F# for Machine Learning Essentials

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

Sudipta Mukherjee

1 customer reviews
Get up and running with machine learning with F# in a fun and functional way
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Book Details

ISBN 139781783989348
Paperback194 pages

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.

Table of Contents

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

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

Authors

Table of Contents

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

Book Details

ISBN 139781783989348
Paperback194 pages
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