Hands-On Machine Learning with C#: Building smarter, speedy and reliable data-intensive applications using machine learning

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Hands-On Machine Learning with C#: Building smarter, speedy and reliable data-intensive applications using machine learning

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內容描述

Explore Supervised, Unsupervised Learning Techniques and Bring Smart Features to your Applications
Key Features

Leverage Machine Learning techniques to build smart, predictive and real-world applications
Accord.Net machine learning framework for reinforcement learning
Machine learning techniques using various libraries-Accord, Numl, Encog

Book Description
In our daily work which is predominantly Information Technology, the necessity of machine learning is everywhere and demanded by all developers, programmers, and analysts. But why C# for machine learning? The answer is most of the Microsoft enterprise applications are written in C# such as Visual Studio, SQL Server, Photoshop and various mobile applications, Unity platform, Microsoft Azure, StackOverflow and so on.
This book develops the intuitive understanding of various concepts, techniques of machine learning and various available machine learning tools through which they can add intelligent features such as sentiment detection, speech recognition, language understanding, smart search and so on to C# and .NET applications.
Using this book, you will implement supervised and unsupervised learning algorithms and will be getting well equipped to create better predictive models. You will learn numerous techniques and algorithms right from a simple linear regression, decision trees, SVM to advanced concepts such as artificial neural networks, autoencoders, and reinforcement learning.
By the end of this book, the readers will develop a machine learning mindset and can leverage the tools, techniques, and packages of C# in building smart, predictive and real-world business applications
What you will learn

Learn how to parameterize a probabilistic problem
Use Naïve Bayes to visually plot and analyze data
Plot a text-based representation of a decision tree using numl
Use the Accord.Net machine learning framework for associative rule-based learning
Develop machine learning algorithms utilizing fuzzy logic
Explore Support Vector Machines for image recognition
Understand Dynamic Time Warping for sequence recognition

Who This Book Is For
This book is meant for all developers and programmers working on a range of platforms from .NET and Windows to mobile devices. Basic knowledge of statistics is required.