Machine Learning Fundamentals

Course Overview

Build a strong foundation in machine learning with a focus on practical implementation. Understand supervised and unsupervised learning algorithms, regression, classification, and model evaluation.

What You Will Learn

• Linear regression, logistic regression, and polynomial models
• Decision trees, random forests, and ensemble methods
• K-means clustering, hierarchical clustering, and DBSCAN
• Principal component analysis and dimensionality reduction
• Model evaluation metrics: accuracy, precision, recall, and F1 score
• Cross-validation and bias-variance tradeoff
• Hands-on projects with Python, pandas, and scikit-learn

Course Highlights

Apply machine learning techniques to real-world datasets across different domains. The programme builds a strong theoretical and practical foundation for advanced ML studies.