Machine Learning shares a platform where a concise foundation to the fundamental concepts in machine learning and popular machine learning algorithms are used. We will wrapup the standards and most popular supervised learning algorithms including linear regression, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks and many more with an establishment to Deep Learning. We will also wrap up the basic congregate algorithms. FRM (Feature reduction methods) will also be discussed during the training. We will help the students to get the basics of computational learning theory. In ourtraining course there will be a discussion on hypothesis space, overfitting, bias and variance and etc. The course will be conducted by hands-on problem solving exercises with programming in Python and some tutorial sessions with the help of the experienced faculties.
Basic programming skills (in Python), algorithm design, basics of probability & statistics.