Focus of this session will be explanation of algorithms available for predictive analytics in Azure Machine Learning service. Algorithms will be grouped by learning style (Supervised, semi-supervised and unsupervised) and will take a look into 1) regression algorithms, 2) Regularization algorithms, 3) Decision trees algorithms, 4) Naive Bayes algorithms, 5) Dimension reduction algorithms, 6) Associated learning (not Kernel) Algorithms and 7) Clustering algorithms. With theory explained we will look into data samples and later examples in ML for these algorithms.
Within this session we will explore, which algorithm is used and useful for what kind of empirical problem and which is suitable for particular data-set.
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