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You're reading from F# for Machine Learning Essentials

Product type Book

Published in Feb 2016

Publisher

ISBN-13 9781783989348

Pages 194 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Sudipta Mukherjee

Table of Contents (16) Chapters

F# for Machine Learning Essentials

Credits

Foreword

About the Author

Acknowledgments

About the Reviewers

www.PacktPub.com

Preface

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

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

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

Information Retrieval

Objective
Different IR algorithms you will learn
What interesting things can you do?
Information retrieval using tf-idf

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

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

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

Index

Challenge yourself!

Now that you know how to use k-NN, logistic regression, and the J48 decision tree to predict classes, can you use whatever you learnt in this chapter to create an e-mail spam identification system? Solve it using all kinds of algorithms and then check your result.

Get the spam data from http://archive.ics.uci.edu/ml/machine-learning-databases/spambase/.