Best Pictures Of The Year 1196405949343956 3

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Simona Dumitrescu

The Best Pictures Of The Year http://siamenglish.com

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Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th. Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow. Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience. Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.

Azure Machine Learning 101

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Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.

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It’s no longer needed supercomputers and a team with PhDs from MIT to create predictive models based on data. We are witnessing innovations in machine learning that are making it an increasingly accessible field. This lecture aims to demystify machine learning through exposure to concepts and use of a number of technologies. In this talk, we will address the types of problems and the algorithms, always applied to real problems. Also, open source tools like Scikit-learn will be presented as well as a way to practice and try these ideas through competitions like Kaggle.

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