The Machine Learning repository contains implementations of several popular machine learning techniques, namely:

• Linear and Logistic Regression
• Support Vector Machines (SVM)
• Decision Trees
• Convolutional Neural Networks (CNN). These implementations were developed as a part of the COL341 course under the guidance of Professor Chetan Arora.

Linear and Logistic Regression models were developed to aid in the evaluation of Microsuturing, a surgical technique used for stitching under a microscope. The aim of this project was to predict the score of Microsuturing performed by trainees based on images, eliminating the need for senior doctors to spend precious time evaluating the images manually.

For the Support Vector Machines implementation, a custom dataset was provided for classification tasks.

Decision Trees were implemented on an image dataset of 2400 samples from 4 distinct classes, including airplane, car, dog, and person-face.

For implementation of Convolution Neural Network, the CIFAR-10 dataset was used, which contains 60,000 32x32 color images in 10 classes, with 6,000 images per class.

Each technique has been organized into its own directory, and instructions for running the code can be found within.