F# for Machine Learning Essentials电子书

F# for Machine Learning Essentials

Table of Contents

F# for Machine Learning Essentials

Credits

Foreword

About the Author

Acknowledgments

About the Reviewers

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Introduction to Machine Learning

Objective

Getting in touch

Different areas where machine learning is being used

Why use F#?

Supervised machine learning

Training and test dataset/corpus

Some motivating real life examples of supervised learning

Nearest Neighbour algorithm (a.k.a k-NN algorithm)

Distance metrics

Decision tree algorithms

Linear regression

Logistic regression

Recommender systems

Unsupervised learning

Machine learning frameworks

Machine learning for fun and profit

Recognizing handwritten digits – your "Hello World" ML program

How does this work?

Summary

2. Linear Regression

Objective

Different types of linear regression algorithms

APIs used

Math.NET Numerics for F# 3.7.0

Getting Math.NET

Experimenting with Math.NET

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

Creating a vector

Creating a matrix

Finding the transpose of a matrix

Finding the inverse of a matrix

Trace of a matrix

QR decomposition of a matrix

SVD 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

3. Classification Techniques

Objective

Different classification algorithms you will learn

Some interesting things you can do

Binary classification using k-NN

How does it work?

Finding cancerous cells using k-NN: a case study

Understanding logistic regression

The sigmoid function chart

Binary classification using logistic regression (using Accord.NET)

Multiclass classification using logistic regression

How does it work?

Multiclass classification using decision trees

Obtaining and using WekaSharp

How does it work?

Predicting a traffic jam using a decision tree: a case study

Challenge yourself!

Summary

4. Information Retrieval

Objective

Different IR algorithms you will learn

What interesting things can you do?

Information retrieval using tf-idf

Measures of similarity

Generating a PDF from a histogram

Minkowski family

L1 family

Intersection family

Inner Product family

Fidelity family or squared-chord family

Squared L2 family

Shannon's Entropy family

Combinations

Set-based similarity measures

Similarity of asymmetric binary attributes

Some example usages of distance metrics

Finding similar cookies using asymmetric binary similarity measures

Grouping/clustering color images based on Canberra distance

Summary

5. Collaborative Filtering

Objective

Different classification algorithms you will learn

Vocabulary of collaborative filtering

Baseline predictors

Basis of User-User collaborative filtering

Implementing basic user-user collaborative filtering using F#

Code walkthrough

Variations of gap calculations and similarity measures

Item-item collaborative filtering

Top-N recommendations

Evaluating recommendations

Prediction accuracy

Confusion matrix (decision support)

Ranking accuracy metrics

Prediction-rating correlation

Working with real movie review data (Movie Lens)

Summary

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

7. Anomaly Detection

Objective

Different classification algorithms

Some cool things you will do

The different types of anomalies

Detecting point anomalies using IQR (Interquartile Range)

Detecting point anomalies using Grubb's test

Grubb's test for multivariate data using Mahalanobis distance

Code walkthrough

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