Mastering .NET Machine Learning : Use Machine Learning in Your .NET Applications
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| Title: | Mastering .NET Machine Learning : Use Machine Learning in Your .NET Applications |
|---|---|
| Description: | Master the art of machine learning with.NET and gain insight into real-world applicationsKey FeaturesBased on.NET framework 4.6.1, includes examples on ASP.NET Core 1.0Set up your business application to start using machine learning techniquesFamiliarize the user with some of the more common.NET libraries for machine learningImplement several common machine learning techniquesEvaluate, optimize and adjust machine learning modelsBook Description.Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their.Net applications smarter. Also,.NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using.NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in.NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions. You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results. Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly. What you will learnWrite your own machine learning applications and experiments using thelatest.NET framework, including.NET Core 1.0Set up your business application to start using machine learning.Accurately predict the future using regressions.Discover hidden patterns using decision trees.Acquire, prepare, and combine datasets to drive insights.Optimize business throughput using Bayes Classifier.Discover (more) hidden patterns using KNN and Naïve Bayes.Discover (even more) hidden patterns using K-Means and PCA.Use Neural Networks to improve business decision making while using the latest ASP.NET technologies.Explore “Big Data”, distributed computing, and how to deploy machine learning models to IoT devices – making machines self-learning and adaptingAlong the way, learn about Open Data, Bing maps, and MBraceWho this book is forThis book is targeted at.Net developers who want to build complex machine learning systems. Some basic understanding of data science is required. |
| Authors: | Jamie Dixon |
| Resource Type: | eBook. |
| Subjects: | Machine learning |
| Categories: | COMPUTERS / Data Science / General, COMPUTERS / Machine Theory, COMPUTERS / Languages / .NET |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf – Type: ebook-epub Text: Availability: 0 |
|---|---|
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| Items | – Name: Title Label: Title Group: Ti Data: Mastering .NET Machine Learning : Use Machine Learning in Your .NET Applications – Name: Abstract Label: Description Group: Ab Data: Master the art of machine learning with.NET and gain insight into real-world applicationsKey FeaturesBased on.NET framework 4.6.1, includes examples on ASP.NET Core 1.0Set up your business application to start using machine learning techniquesFamiliarize the user with some of the more common.NET libraries for machine learningImplement several common machine learning techniquesEvaluate, optimize and adjust machine learning modelsBook Description.Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their.Net applications smarter. Also,.NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using.NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in.NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions. You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results. Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly. What you will learnWrite your own machine learning applications and experiments using thelatest.NET framework, including.NET Core 1.0Set up your business application to start using machine learning.Accurately predict the future using regressions.Discover hidden patterns using decision trees.Acquire, prepare, and combine datasets to drive insights.Optimize business throughput using Bayes Classifier.Discover (more) hidden patterns using KNN and Naïve Bayes.Discover (even more) hidden patterns using K-Means and PCA.Use Neural Networks to improve business decision making while using the latest ASP.NET technologies.Explore “Big Data”, distributed computing, and how to deploy machine learning models to IoT devices – making machines self-learning and adaptingAlong the way, learn about Open Data, Bing maps, and MBraceWho this book is forThis book is targeted at.Net developers who want to build complex machine learning systems. Some basic understanding of data science is required. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jamie+Dixon%22">Jamie Dixon</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+General%22">COMPUTERS / Data Science / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Machine+Theory%22">COMPUTERS / Machine Theory</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Languages+%2F+%2ENET%22">COMPUTERS / Languages / .NET</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 006.31 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Machine learning Type: general Titles: – TitleFull: Mastering .NET Machine Learning : Use Machine Learning in Your .NET Applications Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jamie Dixon – PersonEntity: Name: NameFull: Jamie Dixon IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2016 – D: 10 M: 08 Type: profile Y: 2016 Identifiers: – Type: isbn-print Value: 9781785888403 – Type: isbn-electronic Value: 9781785881190 Titles: – TitleFull: Mastering .NET Machine Learning : Use Machine Learning in Your .NET Applications Type: main |
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