Machine learning service is Microsoft Azure drag and drop tool for building, testing and deploying any kind of predictive model on your data-set. Finalized solution is published and used by daily business in larger stack of your Microsoft Azure services. With easy and interactive creation of models, algorithms and decisions do not tend to be that simple! Especially when one has to make business decision on results.
Focus on this session will be mathematical and graphical explanation of algorithms available for predictive analytics in Azure Machine Learning service. Algorithms – grouped by learning type – will be examined and crossed referenced through all available and ready-to-use. Understanding the the basics – data inference, data splitting, data stratification, to sweeping, to theory of algorithms: regression, decision trees, Clustering and Naive Bayes. We will clarify the confusion over algorithms, suitable data for algorithms and what type of problem can be tackled with.