This repository contains lecture materials, datasets, Colab notebooks, assignments, and visualizations for the course Applied Machine Learning at BUET (Batch 31, 2025).
Course Name: Applied Machine Learning
Course Links: https://cse.buet.ac.bd/web/iac
| Class | Topic |
|---|---|
| 01 | Introduction to Machine Learning, Data Preprocessing, Feature Engineering |
| 02 | Scikit-learn Basics & Model Building |
| 03 | Linear Regression & Evaluation Metrics |
| 04 | Logistic Regression & Classification Metrics |
| 05 | Clustering: K-Means & Hierarchical |
| 06 | Decision Trees & Random Forests |
| 07 | Boosting: AdaBoost, Gradient Boosting, XGBoost |
| 08 | Support Vector Machines & Kernel Methods |
| 09 | Model Evaluation: CV, Overfitting, Sampling |
| 10 | Ensemble Learning Techniques |
| 11 | Neural Networks: Deep Neural Network (DNN) |
| 12 | Convolutional Neural Network (CNN) |
| 13 | Recurrent Neural Network (RNN) & Sequence Modeling |
| 14 | Transformers & Large Language Models (LLMs) |
| 15 | Transfer Learning & Advanced Neural Networks |
This repository is for educational purposes only. You can use, share, and adapt the materials for non-commercial academic purposes.