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MACHNINE LEARNING ALGORITHMS

Machine learning can appear in many guises. We now discuss a number of applications, the types of data they deal with, and finally, we formalize the problems in a somewhat more stylized fashion. The latter is key if we want to avoid reinventing the wheel for every new application. Instead, much of the art of machine learning is to reduce a range of fairly disparate problems to a set of fairly narrow prototypes. Much of the science of machine learning is then to solve those problems and provide good guarantees for the solutions. In this module, the objective would be to cover the most widely used machine learning algorithms.

The dropbox folder contains exercises, research papers and industry cases that will be covered in Machine Learning Algorithms.

              

           

               is a coding software for statistical computing.                               Download here.

                is a programming language.

                Download here.

I have a blog titled review of different softwre packages

Module 0:  Machine Learning Algorithm Modules

Module 1:  Decision Tree Algorithm

Module 2:  Support Vector Machine Algorithm

Module 3:  Naive Bayes Algorithm

Module 4:  K-Nearest Neighbours Algorithm

Module 5:  K-Means Clustering Algorithm

Module 6:  Random Forest Algorithm

Module 7:  Apriori Algorithm

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