Course Description

This course introduces the core principles and techniques of machine learning. Learners will gain hands-on experience building models for regression, classification, clustering, and recommendation systems using Python libraries such as Scikit-learn and TensorFlow. Topics include data preprocessing, model evaluation, overfitting, and hyperparameter tuning. Emphasis is placed on practical applications in domains like healthcare, finance, and e-commerce. A practical introduction to machine learning algorithms, model training, and their applications in real-world problems.

• Who Should Enroll?

  - CS & IT students - Data science enthusiasts - Junior developers

• Why It Matters Now

  ML is a foundational AI skill — in demand across industries from finance to healthcare to marketing.
• Exit Profile

  Graduates will be able to build, test, and deploy basic machine learning models and analyze datasets to extract insights. They will be positioned to take on entry-level roles in data science teams or pursue further specialization.