Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as linear regression, logistic regression, Decision tree, Backpropagation Algorithm, Naïve Bayesian classifier, Bayesian Network, k-Means Algorithm, k-Nearest Neighbour Algorithm.

After studying this course, the students will be able to

1. Understand the implementation procedures for the machine learning algorithms

2. Design Python programs for various Learning algorithms.

3. Apply appropriate data sets to the Machine Learning algorithms

4. Identify and apply Machine Learning algorithms to solve real world problems