Machine Learning
Algorithms that learn patterns from data to make predictions and decisions.
About
Machine Learning (ML) enables systems to learn from data without being explicitly programmed. This course covers supervised and unsupervised learning, model evaluation, optimization, and practical algorithms used in real-world applications.
- Supervised learning: Regression and classification
- Unsupervised learning: Clustering and dimensionality reduction
- Model selection, cross-validation, and regularization
- Neural networks and support vector machines
Units
Unit I
Introduction to ML
ML pipeline, types of learning, data preprocessing, evaluation metrics.
Open Unit →Unit II
Regression & Classification
Linear and logistic regression, loss functions, regularization, metrics.
Open Unit →Unit III
Decision Trees & Ensembles
Entropy/Gini, pruning, Random Forests, Gradient Boosting, XGBoost.
Open Unit →Unit V
SVMs & Neural Networks
Max-margin classifiers, kernels, perceptron, MLP, backpropagation.
Open Unit →