The aim is to get familiar with the fundamental theoretical concepts in machine learning, as well as practical aspects of applying machine learning algorithms.
Key techniques in supervised machine learning are demostrated, such as classification using decision trees and nearest neighbour algorithms, and regression analysis. A particular emphasis is shown on evaluation of the performance of these algorithms. In unsupervised machine learning, a number of popular clustering algorithms are shown in detail.
Apart from these further topics are also covered such as esemble learning and dimension reduction.
Please find different Supervised and Unsupervised Algorithm with examples shown above.