Description

In this course we will have a quick introduction to machine learning and this will not be very deep in a mathematical sense but it will have some amount of mathematical trigger and what we will be doing in this course is covering different paradigms of machine learning and with special emphasis on classification and regression tasks and also will introduce you to various other machine learning paradigms. In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning.

A brief introduction with emphasis on brief right, so the rest of the course would be a more elongated introduction to machine learning right.

So what is machine learning so I will start off with a canonical definition put out by Tom Mitchell in 97 and so a machine or an agent I deliberately leave the beginning undefined because you could also apply this to non machines like biological agents so an agent is said to learn from experience with respect to some class of tasks right and the performance measure P if the learners performance tasks in the class as measured by P improves with experience.

In this course, you’ll learn;

Hypothesis Space and Inductive Bias
Evaluation and Cross-Validation
Linear Regression
Learning Decision Tree
Python Exercise on Decision Tree and Linear Regression
and Much MUch More!!