Diploma Level Course
Machine Learning Practice
This companion course to the ML Theory course introduces the student to scikit-learn, a popular Python machine learning module, to provide hands-on problem solving experience for all the methods and models learnt in the Theory course.
Course ID: BSCS2008
Course Credits: 4
Course Type: Data Science
Pre-requisites: BSCS2004 - Machine Learning Foundations BSCS2007 - Machine Learning Techniques
What you’ll learnVIEW COURSE VIDEOS
Course structure & Assessments
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.
| WEEK 1 | End-to-end machine learning project on scikit-learn |
| WEEK 2 | Graph Theory(VOL 3) |
| WEEK 3 | Regression on scikit-learn - Linear regression Gradient descent - batch and stochastic. |
| WEEK 4 | Polynomial regression, Regularized models |
| WEEK 5 | Logistic regression |
| WEEK 6 | Classification on scikit-learn - Binary classifier |
| WEEK 7 | Classification on scikit-learn - Multiclass classifier |
| WEEK 8 | Support Vector Machines using scikit-learn |
| WEEK 9 | Decision Trees, Ensemble Learning and Random Forests |
| WEEK 10 | Decision Trees, Ensemble Learning and Random Forests (Continued) |
| WEEK 11 | Neural networks models in scikit-learn |
| WEEK 12 | Unsupervised learning |
About the Instructors
Dr. Ashish Tendulkar is a researcher with Google Research Bangalore. He holds Masters and PhD from IIT Bombay. Before his current position, he was an Assistant Professor at IIT Madras and Head of data sciences at Persistent Systems Pune. Ashish is passionate about teaching ML and writing AI related contents in Indian languages.