Machine Learning Course: Assignments and Projects

This repository contains my work for the Machine Learning course, including assignments and projects. It covers fundamental and advanced topics in machine learning, with practical implementations and in-depth explorations.

Assignments

Assignment 1: Regression and Perceptron

• Notebook 1: Implementation of regression models, including linear and polynomial regression.
• Notebook 2: Gradient descent algorithm in perceptrons.

Assignment 2: Supervised and Unsupervised Learning

• Notebook 1: Supervised learning methods, including k-Nearest Neighbors (kNN), ensemble learning algorithms like AdaBoost, and Random Forest.
• Notebook 2: Unsupervised learning methods, including k-Means clustering and dimensionality reduction with Principal Component Analysis (PCA).

Assignment 3: Neural Networks and Optimization

• Notebook 1: Implementation of a Multi-Layer Perceptron (MLP) from scratch using NumPy for the MNIST dataset.
• Notebook 2: Neural network model for function approximation.
• Notebook 3: Study of optimization methods, including momentum, mini-batch gradient descent, and other techniques.

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This repository serves as a comprehensive resource for exploring machine learning concepts, demonstrating hands-on implementations, and documenting my progress in this field.