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F♯ for Machine Learning Essentials.
Get up and running with machine learning with F♯ in a fun and functional wayAbout This Book Design algorithms in F♯ to tackle complex computing problems Be a proficient F♯ data scientist using this simple-to-follow guide Solve real-world, data-related problems with robust statistical models, built f...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited
Feb. 2016.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 520 | 8 | |a Get up and running with machine learning with F♯ in a fun and functional wayAbout This Book Design algorithms in F♯ to tackle complex computing problems Be a proficient F♯ data scientist using this simple-to-follow guide Solve real-world, data-related problems with robust statistical models, built for a range of datasetsWho This Book Is ForIf you are a C# or an F♯ developer who now wants to explore the area of machine learning, then this book is for you. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage. What You Will Learn Use F♯ to find patterns through raw data Build a set of classification systems using Accord.NET, Weka, and F♯ Run machine learning jobs on the Cloud with MBrace Perform mathematical operations on matrices and vectors using Math.NET Use a recommender system for your own problem domain Identify tourist spots across the globe using inputs from the user with decision tree algorithmsIn DetailThe 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. Style and approachThis book is a fast-paced tutorial guide that uses hands-on examples to explain real-world applications of machine learning. Using practical examples, the book will explore several machine learning techniques and also describe how you can use F♯ to build machine learning systems. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a Recognizing handwritten digits -- your ""Hello World"" ML programHow 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. | |
| 505 | 8 | |a 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? | |
| 505 | 8 | |a Finding cancerous cells using k-NN: a case studyUnderstanding 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? | |
| 505 | 8 | |a Information retrieval using tf-idfMeasures 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. | |
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