Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying F# for Machine Learning Essentials
  • Table Of Contents Toc
F# for Machine Learning Essentials

F# for Machine Learning Essentials

By : Sudipta Mukherjee
2 (1)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Introduction to Machine Learning
Icon
1. Introduction to Machine Learning
Icon
Objective
Icon
Why use F#?
Icon
Unsupervised learning
Icon
Machine learning frameworks
Icon
Machine learning for fun and profit
Icon
Recognizing handwritten digits – your "Hello World" ML program
Icon
Summary
2
2. Linear Regression
Icon
2. Linear Regression
Icon
Objective
Icon
Different types of linear regression algorithms
Icon
APIs used
Icon
The basics of matrices and vectors (a short and sweet refresher)
Icon
QR decomposition of a matrix
Icon
Linear regression method of least square
Icon
Finding linear regression coefficients using F#
Icon
Finding the linear regression coefficients using Math.NET
Icon
Putting it together with Math.NET and FsPlot
Icon
Multiple linear regression
Icon
Multiple linear regression and variations using Math.NET
Icon
Weighted linear regression
Icon
Plotting the result of multiple linear regression
Icon
Ridge regression
Icon
Multivariate multiple linear regression
Icon
Feature scaling
Icon
Summary
3
3. Classification Techniques
Icon
3. Classification Techniques
Icon
Objective
Icon
Different classification algorithms you will learn
Icon
Some interesting things you can do
Icon
Understanding logistic regression
Icon
Multiclass classification using logistic regression
Icon
Multiclass classification using decision trees
Icon
Predicting a traffic jam using a decision tree: a case study
Icon
Challenge yourself!
Icon
Summary
4
4. Information Retrieval
Icon
4. Information Retrieval
Icon
Objective
Icon
Different IR algorithms you will learn
Icon
What interesting things can you do?
Icon
Information retrieval using tf-idf
5
5. Collaborative Filtering
Icon
5. Collaborative Filtering
Icon
Objective
Icon
Different classification algorithms you will learn
Icon
Vocabulary of collaborative filtering
Icon
Baseline predictors
Icon
Item-item collaborative filtering
Icon
Top-N recommendations
Icon
Evaluating recommendations
Icon
Ranking accuracy metrics
Icon
Working with real movie review data (Movie Lens)
Icon
Summary
6
6. Sentiment Analysis
Icon
6. Sentiment Analysis
Icon
Objective
Icon
What you will learn
Icon
A baseline algorithm for SA using SentiWordNet lexicons
Icon
Handling negations
Icon
Identifying praise or criticism with sentiment orientation
Icon
Pointwise Mutual Information
Icon
Using SO-PMI to find sentiment analysis
Icon
Summary
7
7. Anomaly Detection
chevron up
Icon
7. Anomaly Detection
Icon
Objective
Icon
Detecting point anomalies using IQR (Interquartile Range)
Icon
Detecting point anomalies using Grubb's test
Icon
Grubb's test for multivariate data using Mahalanobis distance
Icon
Chi-squared statistic to determine anomalies
Icon
Detecting anomalies using density estimation
Icon
Strategy to convert a collective anomaly to a point anomaly problem
Icon
Dealing with categorical data in collective anomalies
Icon
Summary
8
Index
Icon
Index

Summary

Anomaly detection is a very active field of research because what's anomalous now may not remain anomalous forever. This poses a significant challenge to designing a good anomaly detection algorithm. Although the algorithms discussed in this chapter mostly deal with point anomalies, they can be also used to detect sequential anomalies with a little bit of feature extraction.

Sometimes, anomaly detection is treated as a classification problem, and several classification algorithms such as k-NN, SVM, and Neural Networks are deployed to identify anomalous entries. The challenge, however, is to get well-labeled data. However, some heuristics are used to assign a score called the anomaly score to each data element, and then the top few with the highest anomaly scores (sometimes above a given threshold) are determined to be anomalous.

Anomaly detection has several applications, such as finding imposters using anomaly detection on keyboard dynamics, pedestrians, and landmine detection from...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
F# for Machine Learning Essentials
End of Chapter 7
Next Chapter
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon