Machine Learning

Over the past two decades Machine Learning has become one of the mainstays of information technology and with that, a rather central, albeit usually hidden, part of our life. With the ever increasing amounts of data becoming available there is good reason to believe that smart data analysis will becomeeven more pervasive as a necessary ingredient for technological progress.

Machine Learning types:

Supervised ML
             Linear Regression
             Logistics Regression
             Polynomial Regression
             Confusion Matrix
             KNN(Klustering Nearest Neighbour)/(uses K-mean algorithm)
             Decision Tree
             Random Forest tree(Group of Decision Trees)
             SVM(Support Vector Machine) [Accuracy more than KNN]
             One Hot Encoding
             Naive Bayes Classifier
2- Unsupervised ML
             K-mean Algorithm
             Multi Regression
3- Reinforcement ML (Used in Artificial Intelligence)
             Data is trained with respect to time but eliminates/removes risks in the future for further using unsupervised ML.

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.