This course provides a comprehensive introduction to Machine Learning, covering fundamental concepts, algorithms, and real-world applications. Students will explore supervised and unsupervised learning, deep learning, and model evaluation techniques. Practical hands-on exercises using Python will reinforce theoretical knowledge.
Module Outline:
Introduction to Machine Learning – Overview, types of ML, and key applications.
Data Preprocessing & Feature Engineering – Data cleaning, transformation, and selection.
Supervised Learning – Regression, classification, and model evaluation.
Unsupervised Learning – Clustering, dimensionality reduction, and anomaly detection.
Deep Learning Basics – Neural networks, backpropagation, and optimization.
Model Evaluation & Optimization – Overfitting, bias-variance tradeoff, and hyperparameter tuning.
Machine Learning in Practice – Case studies, deployment, and ethical considerations.
