F♯ for machine learning essentials : get up and running with machine learning with F♯ in a fun and functional way /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mukherjee, Sudipta
Otros Autores: Herbrich, Ralf
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai [India] : Packt Publishing, 2016.
Colección:Community experience distilled.
Temas:
F♯ (Computer program language)
Machine learning.
Apprentissage automatique.
COMPUTERS / Databases / General.
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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?.
  • 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.
  • 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.

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