This repository contains the implementation of some basic concepts of Machine Learning as part of PRML lab work. The list of topics are as follows:
- Lab_01: Probability Theory
- Lab_02: Linear Algebra
- Lab_03: Convex Optimization
- Lab_04: Clustering Part 1 (Kmeans, GMM)
- Lab_05: Clustering Part 2 (DBSCAN, Heirarchical)
- Lab_06: Regression Part 1 (Linear (Univariate and Multivariate))
- Lab_07: Regression Part 2 (Polynomial, Logistic)
- Lab_08: Classification (KNN, SVM)
- Lab_09: Dimesionality Reduction (PCA, LDA)
- Lab_10: Naive Bayes Classifier
- Lab_11: Hidden Markov Model (HMM)
The Folder contains the following files:
- Skeleton notebook file (*.ipynb)
- PDF version of skeleton notebook file (*.pdf)
- Additional datasets if used in notebook
- Solution notebook file (*_Solution.ipynb)