What Azure Machine Learning Algorithm Should You Use

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Azure Machine Learning Studio comes with a large number of machine learning algorithms that you can use to solve predictive analytics problems. The infographic below demonstrates how the four types of machine learning algorithms – regression, anomaly detection, clustering, and classification – can be used to answer your machine learning questions. The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms. To download the cheat sheet and follow along with this article, go to Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio. This cheat sheet is perfect for students its aimed at someone with undergraduate-level machine learning, trying to choose an algorithm to start with in Azure Machine Learning Studio.


Anomaly detection using built-in machine learning models in Azure Stream Analytics

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Built-in machine learning (ML) models for anomaly detection in Azure Stream Analytics significantly reduces the complexity and costs associated with building and training machine learning models. This feature is now available for public preview worldwide both in the cloud and on IoT Edge. Azure Stream Analytics is a fully managed serverless PaaS offering on Azure that enables customers to analyze and process fast moving streams of data, and deliver real-time insights for mission critical scenarios. Developers can use a simple SQL language (extensible to include custom code) to author and deploy powerful analytics processing logic that can scale-up and scale-out to deliver insights with milli-second latencies. Many customers use Azure Stream Analytics to continuously monitor massive amounts of fast-moving streams of data in order to detect issues that do not conform to expected patterns and prevent catastrophic losses.


Student and Faculty Guide – 10 easy steps to get up and running with Azure Machine Learning

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My colleague Amy Nicholson is the UK expert on Azure Machine Learning, the following blog post is after a quizzing session to get understand how to get started with Azure Machine Learning" Each student receives $100 of Azure credit per month, for 6 months. The Faculty member receives $250 per month, for 12 months. The Azure machine learning team provided a very nice walkthrough tutorial which covers a lot of the basics. This tutorial is really useful as it takes you through the entire process of creating an AzureML workspace, uploading data, creating an experiment to predict someone's credit risk, building, training, and evaluating the models, publishing your best model as a web service, and calling that web service. Now you need to learn how to import a data set into Azure Machine Learning, and where to find interesting data to build something amazing.


An Introduction to AI-powered Microsoft Tools

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Microsoft is nowadays one of the major providers for AI powered cloud services. In fact, according to a RightScale's survey carried out in 2018, Microsoft Azure Cloud services are currently second just to Amazon AWS (Figure 1). In this article, I will be considering Microsoft as case study as Microsoft CEO Satya Nadella recently shared Microsoft interest to make AI a vital part of their business [1]. I will now introduce you to some of the different Microsoft tools which are currently available and some alternatives provided by the completion. Finally, we will focus on what are going to be next steps in research.