| Description |
1 online resource (194 pages) : color illustrations, tables. |
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text file |
| Series |
Community Experience Distilled
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Community experience distilled.
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| Note |
Includes index. |
| Contents |
Cover -- Copyright -- Credits -- Foreword -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 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 -- Unsupervised learning -- Machine learning frameworks -- Machine learning for fun and profit -- Recognizing handwritten digits -- your "Hello World" ML program -- How does this work? -- Summary -- Chapter 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 -- Chapter 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?. |
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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 -- Chapter 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 -- 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 -- Chapter 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 -- Chapter 6: Sentiment Analysis -- Objective -- What you will learn -- A baseline algorithm for SA using SentiWordNet lexicons. |
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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 -- 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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
F♯ (Computer program language)
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F♯ (Computer program language) |
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Machine learning.
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Machine learning. |
| Genre/Form |
Electronic books.
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| Added Author |
Herbrich, Ralf.
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| Other Form: |
Print version: Mukherjee, Sudipta. F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way. Birmingham, England ; Mumbai, [India] : Packt Publishing, ©2016 x, 169 pages Community experience distilled. 9781783989348 |
| ISBN |
9781783989355 (electronic book) |
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1783989351 (electronic book) |
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9781783989348 |
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1783989343 |
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1783989351 |
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