Welcome to my repository of machine learning assignments completed during my university coursework. This repository is organized into assignments, each focusing on specific machine learning topics. Below is an overview of the topics covered in each assignment:

• Linear Regression and Gradient Descent 📈💡:

  • Understanding the principles of linear regression and the optimization process using gradient descent.

• Activation Functions 🔄:

  • Exploring different activation functions used in neural networks and their impact on model performance.

• Logistic Regression 📊:

  • Applying logistic regression for binary classification problems and understanding its use cases.

• Decision Tree and Random Forest 🌲🌳:

  • Building decision trees and understanding ensemble methods, particularly the random forest algorithm.

• Support Vector Machine (SVM) 🤖🛠:

  • Exploring the principles behind SVMs for classification tasks and understanding the role of kernels.

• Bayes Theorem 🧮:

  • Understanding the Bayesian approach and its application in machine learning problems.

• Naive Bayes Classifier 📚🤔:

  • Implementing the Naive Bayes classifier and its use in text classification and other applications.

• K-Mean and K-Medoid Clustering 🌐🔍:

  • Exploring unsupervised learning through clustering with K-Means and K-Medoid algorithms.

• Artificial Neural Network (Backpropagation) 🧠🔄:

  • Understanding the architecture of artificial neural networks and the backpropagation algorithm for training.

• Convolutional Neural Network (CNN) 🖼️🕵️‍♂️:

  • Implementing CNNs for image classification tasks and understanding convolutional layers, pooling, and feature extraction.

The repository follows a consistent structure with each assignment containing both the assignment questions and solutions. Navigate to the specific assignment folder to access the questions in the PDF files and explore the solution code along with detailed reports.

Feel free to use this repository as a resource for learning and reference. If you have any questions or suggestions, feel free to reach out!

Happy learning! 🚀