Explore supervised and unsupervised learning techniques and add smart features to your applications.
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.
What you will learn 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
Key Features 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
Who This Book Is For Hands-On Machine Learning with C#is forC# .NETdevelopers who work on a range of platforms from .NET and Windows to mobile devices. Basic knowledge of statistics is required.
Table of Contents Machine Learning Basics ReflectInsight – Real-Time Monitoring Bayes Intuition - Solving the 'Hit and Run' Mystery and Performing Data Analysis Risk versus Reward - Reinforcement Learning Navigating the obstacle course with Fuzzy Logic Color Blending – Self-Organizing Maps and Elastic Neural Networks Facial and Motion Detection - Imaging Filters and Motion Area Highlighting with Accord .Net Encyclopedias and Neurons Should I Take the Job - Decision Trees in Action Deep Belief Networks Using SharpRBM Micro-Benchmarking and Activation Functions Intuitive Deep Learning in C# .Net Quantum Computing - The Future
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Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
This book is a hands-on introduction to learning algorithms. It is for people who may know a little machine learning (or not) and who may have heard about TensorFlow, but found the documentation too daunting to approach.
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