Table of Contents

Course structure

Week 1 - Introduction to Machine Learning

Week 2 - Regression

Week 3 - Classification

Week 4 - Clustering

Week 5 - Time Series

Week 6 - Recommender System

Additional pages

Links to webinars

List of assignments

In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on.

You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.

In this week, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.

In this week, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.