Machine-Learning-and-Data-Science

This repository contains Python codes to implement the following:

1. Supervised Machine Learning Algorithms using Scikit-Learn
2. Unsupervised Machine Learning Algorithms using Scikit-Learn and SciPy
3. Basic Neural Networks using PyTorch
4. General Data Pre-processing and Visualization Techniques using Matplotlib and Seaborn

It consists the implementation and execution of the following ML algorithms (as of 06/20/2021):

1. Gradient Descent
2. Linear Regression using Closed Form
3. Logistic Regression (using Gradient Descent) and its comparision with Logistic Regression using Scikit-Learn
4. SVM (Support Vector Machines) using Scikit-Learn
5. Naive Bayes (Scikit-Learn)
6. K-Means Clustering for Color Quantization (using Scikit-Learn)
7. Hierarchical Clustering (Single Linkage, Complete Linkage and Average Linkage) using SciPy