MLDL 102 – Supervised Learning Models

(Weeks 3–4 | Lecture 6 Hrs / Lab 18 Hrs / Ext 0 Hrs | 24 Total Contact Hrs | 1.0 Semester
Credit)
Students will:

• Train linear and logistic regression models for classification and regression.
• Apply decision trees and random forest algorithms for real-world problems.
• Perform feature selection and hyperparameter tuning to optimize models.
• Deploy ensemble learning techniques to enhance prediction performance.
  Prerequisite: MLDL 101 – Machine Learning Foundations
  Tools: Scikit-learn, Pandas, Matplotlib