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Hands-On Machine Learning with C#

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  • Learn to parameterize a probabilistic problem
  • Use Naive 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

The necessity for machine learning is everywhere, and most production enterprise applications are written in C# using tools such as Visual Studio, SQL Server, and Microsoft Azur2e. Hands-On Machine Learning with C# uniquely blends together an understanding of various machine learning concepts, techniques of machine learning, and various available machine learning tools through which users can add intelligent features.These tools include image and motion detection, Bayes intuition, and deep learning, to C# .NET applications.

Using this book, you will learn to implement supervised and unsupervised learning algorithms and will be better equipped to create excellent predictive models. In addition, you will learn both supervised and unsupervised forms of regression, mainly logistic and linear regression, in depth. Next, you will use the nuML machine learning framework to learn how to create a simple decision tree. In the concluding chapters, you will use the Accord.Net machine learning framework to learn sequence recognition of handwritten numbers using dynamic time warping. We will also cover advanced concepts such as artificial neural networks, autoencoders, and reinforcement learning.

By the end of this book, you will have developed a machine learning mindset and will be able to leverage C# tools, techniques, and packages to build smart, predictive, and real-world business applications.

  • Leverage machine learning techniques to build real-world applications
  • Use the Accord.NET machine learning framework for reinforcement learning
  • Implement machine learning techniques using Accord, nuML, and Encog
Page Count 274
Course Length 8 hours 13 minutes
ISBN 9781788994941
Date Of Publication 24 May 2018


Matt R. Cole

Matt R. Cole is a developer and author with 30 years' experience. Matt is the owner of Evolved AI Solutions, a provider of advanced Machine Learning/Bio-AI, Microservice and Swarm technologies. Matt is recognized as a leader in Microservice and Artificial Intelligence development and design. As an early pioneer of VOIP, Matt developed the VOIP system for NASA for the International Space Station and Space Shuttle. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. In his spare time Matt authors books, and continues his education taking every available course in advanced mathematics, AI/ML/DL, Quantum Mechanics/Physics, String Theory and Computational Neuroscience.