The Applied Machine Learning course at the Bangladesh-Korea Information Access Center (BK-IAC) is designed for professionals aiming to delve into the practical applications of machine learning. It covers essential concepts, classical ML algorithms, and recent advancements in the field. Participants will gain hands-on experience with frameworks like scikit-learn, TensorFlow/Keras, XGBoost, DNN, CNN, RNN, and more.
We expect participants to have prior programming knowledge, specifically in Python. If you lack this background, please learn the basics of Python before starting this course. This course does not cover Python fundamentals, and enrolling without adequate Python knowledge may lead to significant difficulties in understanding the content, resulting in a poor learning experience. This responsibility lies solely with the participant. We have also offered an "Introduction to Python" course (Course Details Link), which you can complete prior to enrolling in this course.
The course length will be 8 weeks with two classes in each week and 3 hours in each class. The tentative lecture plan of the course is as follows:
Class# | Content |
---|---|
1 | Introduction to Machine Learning, Data Preprocessing and Feature Engineering |
2 | Introduction to scikit-learn and Model Building Basics |
3 | Linear Regression and Model Evaluation Metrics |
4 | Logistic Regression and Classification Metrics |
5 | Clustering Techniques: K-Means, Hierarchical Clustering |
6 | Decision Trees and Random Forests |
7 | Boosting Techniques: AdaBoost, Gradient Boosting, XGBoost |
8 | Support Vector Machines (SVM) and Kernel Methods |
9 | Evaluation of Machine Learning Models (Cross-validation, overfitting, learning curve, over sampling/under sampling, etc.) |
10 | Ensemble Learning Techniques |
11 | Introduction to Neural Networks: Deep Neural Network (DNN) |
12 | Convolutional Neural Network (CNN) and Hands-on Practice |
13 | Recurrent Neural Network (RNN) and Sequence Modeling |
14 | Transformer Models, Large Language Models (LLMs) |
15 | Introduction to Transfer Learning and Advanced Topics in Neural Networks |
16 | Final Exam |
Email: iac@cse.buet.ac.bd
Phone: 9665650-80 Ext-6438
Mobile: 01552015596