F For Machine Learning Essentials
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F# for Machine Learning Essentials
Author: Sudipta Mukherjee
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-02-25
Get up and running with machine learning with F# in a fun and functional way About 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 datasets Who This Book Is For If 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 algorithms In Detail 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. Style and approach This 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.
Machine Learning Essentials and it's Application
Author: Prof. Yogendra Kumar
language: en
Publisher: Academic Guru Publishing House
Release Date: 2024-08-05
The book "Machine Learning Essentials and Its Applications" is an informative investigation of the basic concepts of machine learning as well as the many applications of this fascinating field. The fundamental ideas, methods, and algorithms that provide the foundation of machine learning are presented in this book in a format that is designed to lead readers through the process. In order to ensure that the reader has a complete grasp of the discipline, it covers a broad variety of topics, such as supervised and unsupervised learning, neural networks, natural language processing, and computer vision. In addition to providing theoretical information, the book has an emphasis on practical applications, demonstrating how machine learning can be used in a variety of fields, including healthcare, finance, transportation, and entertainment, among others. Every chapter contains case studies and hands-on activities to help readers get a more in-depth grasp of the subject matter and to motivate them to apply what they have learnt in the classroom to situations that they will encounter in the real world. The purpose of this book is to serve as a vital resource for everyone who is interested in understanding the transformational potential of machine learning. It was designed for students, instructors, and industry experts. The book "Machine Learning Essentials and Its Applications" is a necessary travel companion on iv the path to becoming an expert in this rapidly evolving topic since it provides lucid explanations, examples that illustrate the concepts, and important insights.
F# for Machine Learning Essentials
Author: Sudipta Mukherjee
language: en
Publisher: Packt Publishing
Release Date: 2016-02-25
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.