This repository contains implementations of a variety of machine learning algorithms, ranging from traditional statistical methods to deep learning models. It includes:
- KNN (K-Nearest Neighbors)
- K-Means Clustering
- GMM (Gaussian Mixture Model)
- Linear Regression
- PCA (Principal Component Analysis)
- MLP (Multilayer Perceptron)
- CNN (Convolutional Neural Network)
- KDE (Kernel Density Estimation)
- HMM (Hidden Markov Model)
- RNN (Recurrent Neural Network)
- Auto Encoders
The directory structure is as follows:
.
├── README.md
├── assignments
│ ├── 1
│ ├── 2
│ ├── 3
│ ├── 4
│ └── 5
├── data
│ ├── README.md
│ ├── data_setup
│ ├── external
│ ├── interim
│ └── processed
├── models
│ ├── AutoEncoders
│ ├── HMM
│ ├── KDE
│ ├── MLP
│ ├── README.md
│ ├── cnn
│ ├── gmm
│ ├── k_means
│ ├── knn
│ ├── linear_regression
│ └── pca
└── performance_measures
├── README.md
└── performance_measures.py
The classes for the models are in the models folder, the data is present in the data folder and the tasks and analysis on the models is done in the assignments folder. A detailed report is present for each assignment in the README.md file for each assignment.