Lectures
Week # 01 - 09 Feb - Introduction to Machine Learning
Lecture 1- Part 1
Lecture 1- Part 2
Lecture 1- Part 3
Lecture 1- Part 4
Lecture 1- Part 5
Lecture 1- Part 6
Lecture 1- Part 7
Lecture 1- Part 8
Week # 02 - 16 Feb - Data Preprocessing + Homework 1: Python Basics
Lecture 2- Part 1
Lecture 2- Part 2
Lecture 2- Part 3
Lecture 2- Part 4
Week # 03 - 23 Feb - Feature Selection and Engineering + Homework 2: Data Preprocessing
Lecture 3- Part 1
Lecture 3- Part 2
Week # 04 - 02 Mar - Classification
Classification Lecture 4- Part 1
Classification Lecture 4- Part 2
Classification Lecture 4- Part 3
No Code Classification Lecture 4- Part 4
No Code Classification Lecture 4- Part 5
No Code Classification Lecture 4- Part 6
Low Code Classification Lecture 4- Part 7
Low Code Classification Lecture 4- Part 8
Low Code Classification Lecture 4- Part 9
Low Code Classification Lecture 4- Part 10
Week # 05 - 09 Mar - Regression + Homework 3: Classification
Regression Lecture 5- Part 1
No Code Regression Lecture 5- Part 2
No Code Regression Lecture 5- Part 3
No Code Regression Lecture 5- Part 4
Low Code Regression Lecture 5- Part 5
Low Code Regression Lecture 5- Part 6
Low Code Regression Lecture 5- Part 7
Low Code Regression Lecture 5- Part 8
Low Code Regression Lecture 5- Part 9
Advanced Concepts in Linear Regression Lecture 5- Part 10
Week # 06 - 16 Mar - Time Series Forecasting + Homework 4: Regression
Lecture 6- Part 1
Lecture 6- Part 2
Lecture 6- Part 3
Lecture 6- Part 4
Week # 07 - 23 Mar - Clustering + Homework 5: Time Series Forecosting
Lecture 7- Part 1
Lecture 7- Part 2
Lecture 7- Part 3
Lecture 7- Part 4
Lecture 7- Part 5
Lecture 7- Part 6
Week # 08 - 30 Mar - AutoML & Computer vision + Homework 6: Clustering + START PREPARING FOR PROJECT PROPOSAL
AutoML Lecture 8- Part 1
Computer vision Lecture 8- Part 1
Computer vision Lecture 8- Part 2
Computer vision Lecture 8- Part 3
Computer vision Lecture 8- Part 4
Computer vision Lecture 8- Part 5
Computer vision Lecture 8- Part 6
Computer vision Lecture 8- Part 7
Computer vision Lecture 8- Part 8
Week # 09 - 06 Apr - Model Explainability & Large Language Model + Homework 7: AutoML + SUBMIT PROJECT PROPOSAL
Lecture 9 - Part 1
Lecture 9- Part 2
Lecture 9- Part 3
Lecture 9- Part 4
Lecture 9- Part 5
Lecture 9- Part 6
Lecture 9- Part 7
Lecture 9- Part 8
Lecture 9 - Part 9
Week # 10 - 13 Apr - Neural Network + Homework 8: Computer Vision + PROJECT SUBMISSION
Lecture 10- Part 1
Lecture 10- Part 2
Lecture 10- Part 3
Lecture 10- Part 4
Lecture 10- Part 5
Lecture 10- Part 6
Lecture 10- Part 7
Lecture 10- Part 8
Week # 11 - 20 Apr - Interpretability
Lecture 11 - Part 1
Lecture 11 - Part 2
Lecture 11 - Part 3
Lecture 11 - Part 4
Week # 12 - 27 Apr - Specialized Topics + START PREPARING FOR POSTER
Tutorial on Random Forest
Tutorial in LightGBM including hyperparameter tuning
Introduction to XGBoost Algorithm
Simple and multivariate linear regression
No Code
Low code I – Model creation
Low code II – Model evaluation
Random Forest
LightGBM
Week # 13 - 04 May - Kaggle Competition
will be informed soon.