## About the course

Professor Sudeshna Sarkar, a faculty of the department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, has designed this course on Machine Learning. This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. Dr. Sarkar has worked with our development team to create the quality course content for the learners. This course comprises of video lectures which can be viewed online and offline as per the convenience of the user. The unique thing about this course is that, this course is accompanied by hands-on problem solving with programming in Python along with the lectures.

This course covers the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. It also covers the basic clustering algorithms. Feature reduction methods will also be discussed. This course also introduces the basics of computational learning theory. Also, this course covers various issues related to the application of machine learning algorithms. This course also covers hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation.