Hands-On Machine Learning with C# : Build Smart, Speedy, and Reliable Data-intensive Applications Using Machine Learning

**Explore supervised and unsupervised learning techniques and add smart features to your applications****About This Book**- 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.**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**In Detail**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.**Style and approach**A step-by-step approach to learning machine learning concepts and techniques with practical implementations BExplore supervised and unsupervised learning techniques and add smart features to your applications/bh2About This Book/h2ulliLeverage machine learning techniques to build real-world applications/liliUse the Accord.NET machine learning framework for reinforcement learning/liliImplement machine learning techniques using Accord, nuML, and Encog/li/ulh2Who This Book Is For/h2Hands-On Machine Learning with Cis forC .NETdevelopers who work on a range of platforms from .NET and Windows to mobile devices. Basic knowledge of statistics is required.h2What You Will Learn/h2ulliLearn to parameterize a probabilistic problem/liliUse Naive Bayes to visually plot and analyze data/liliPlot a text-based representation of a decision tree using nuML/liliUse the Accord.NET machine learning framework for associative rule-based learning/liliDevelop machine learning algorithms utilizing fuzzy logic/liliExplore support vector machines for image recognition/liliUnderstand dynamic time warping for sequence recognition/li/ulh2In Detail/h2The 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.h2Style and approach/h2A step-by-step approach to learning machine learning concepts and techniques with practical implementations Explore supervised and unsupervised learning techniques and add smart features to your applications About This Book • 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. 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 In Detail 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. Style and approach A step-by-step approach to learning machine learning concepts and techniques with practical implementations Annotation Explore supervised and unsupervised learning techniques and add smart features to your applicationsKey FeaturesLeverage machine learning techniques to build real-world applicationsUse the Accord.NET machine learning framework for reinforcement learningImplement machine learning techniques using Accord, nuML, and EncogBook DescriptionThe 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 learnLearn to parameterize a probabilistic problemUse Naive Bayes to visually plot and analyze dataPlot a text-based representation of a decision tree using nuMLUse the Accord.NET machine learning framework for associative rule-based learningDevelop machine learning algorithms utilizing fuzzy logicExplore support vector machines for image recognitionUnderstand dynamic time warping for sequence recognitionWho this book is forHands-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 La 4è de couv. indique : "In our daily work, the necessity for machine learning is everywhere, demanded by all developers, programmers, and analysts. But why C# for machine learning? The answer is that most production enterprise applications are written in C# using tools such as Visual Studio, SQL Server, and Microsoft Azure. This books develops an intuitive understanding of various concepts, the techniques of machine learning, and various available machine learning tools available, through which users can add intelligent features, such as image and motion detection, Bayes intuition, and deep learning, and deep belief, 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. You will learn numerous techniques and algorithms, right from simple linear regression, decision trees, and SVMs, to 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." Chapter 7: Facial and Motion Detection - Imaging Filters; Facial detection; Motion detection; Adding detection to your application; Summary; Chapter 8: Encyclopedias and Neurons - Traveling Salesman Problem; Traveling salesman problem; Learning rate parameter; Learning radius; Summary; Chapter 9: Should I Take the Job - Decision Trees in Action; Decision tree; Decision node; Decision variable; Decision branch node collection; Should I take the job?; numl; Accord.NET decision trees; Learning code; Confusion matrix; True positives; True negatives; False positives; False negatives; Recall