Course Outcomes
CO1 Ability to conduct exploratory data analysis
CO2 Apply the complete ML pipeline in real-world dataset – Analyse datasets, decide pre-processing steps, visualize data, apply ML models, and infer the meaning based on different performance metrics.
Course contents
Introduction to machine learning and machine learning applications. Data featurization, vectorization, linear algebra, and matrix representations. Supervised learning – linear regression, polynomial regression, logistic regression, Decision Trees, Support Vector Machine and ANN. Regularization, tuning, overfitting, underfitting. Unsupervised learning: Clustering, dimensionality reduction (PCA).
Deep Neural networks: multilayer perceptron, transfer learning, edge models. ML model evaluation metrics. Generative AI – LLMs. MLOps – introduction to converting ML models from test bench to production (saving, loading, using trained models).