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    C#/ML.NET Machine Learning MasterClass

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    Website https://glceurope.com/machine-learning-masterclass-2-details/?utm_source=wikicfp&utm_medium=media_pa | Edit Freely

    Category machine learning

    Deadline: September 09, 2020 | Date: September 10, 2020-September 11, 2020

    Venue/Country: Radisson Blu Park Royal Palace Hotel, Vienna, Austria

    Updated: 2020-05-05 16:33:05 (GMT+9)

    Call For Papers - CFP

    In this 2-day training you will learn how to build machine learning applications in C# with Microsoft’s new ML.NET library. You will learn how to prepare a data set, load and process it, and design and train a machine learning model to generate useful predictions from the data. The course will provide a solid foundation of machine learning (regression, classification and clustering) and also cover advanced applications like using deep convolutional neural networks to detect objects in images.

    You will learn how to build advanced AI applications with only a few lines of C# code. We will cover many use cases like trend prediction, anomaly detection, text analysis, computer vision, and many others.

    During the course Mark Farragher will share many tips and tricks on how you can start introducing AI and data science functionality to your business today.

    Technology Platform

    This course uses NET Core version 3.0 and Microsoft Visual Studio Code. Both Windows 10, OS/X, and Ubuntu Linux are supported. The source code of all demos will be made available to participants.

    By the end of the course, the participants will:

    Have experienced C# machine learning first-hand

    Understand the capabilities of the Microsoft ML.NET machine learning library

    Be able to build classifiers, anomaly detectors, recommendation systems, and many other AI apps

    Discover how to run deep convolutional neural networks in C#

    Have learned about regression, classification, clustering, association mining, and more

    Be able to quickly build AI business prototypes in C#

    Fully understand the role of AI in the Microsoft developer ecosystem


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
    Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.