This repository contains the source codes and implementations of various machine learning algorithms completed during the Machine Learning course at Sharif University of Technology (Prof: Seyed Abolfazl Motahari). Each section represents a different topic or technique covered in the course, and the projects demonstrate practical applications of the algorithms.

• Description: Implementation of linear regression models for single and multiple variables.
• Source Code: Linear_Regression

• Description: Implementation of both linear and quadratic discriminant analysis for classification tasks.
• Source Code: LDA_QDA

• Description: Implementation of logistic regression for binary classification problems.
• Source Code: Logistic_Regression

• Description: Implementation of the Naive Bayes algorithm for probabilistic classification.
• Source Code: Naive_Bayes

• Description: Implementation of Lasso (L1 regularization) and Ridge (L2 regularization) regression models.
• Source Code: Lasso_Ridge_Regression

• Description: Implementation of decision tree classifier, bagging, and AdaBoost ensemble methods.
• Source Code: Decision_Tree_Ensemble

• Description: Implementation of polynomial regression and spline models to capture non-linear relationships.
• Source Code: Beyond_Linearity

• Description: Implementation of Support Vector Machine, Principal Component Analysis, K-Means clustering, and basic Neural Networks.
• Source Code: SVM_PCA_KMeans_NeuralNet

• Description: A project focusing on semantic analysis tasks, such as sentiment analysis, text classification, or topic modeling.
• Source Code: Semantic_Analysis

1. Clone the repository:

git clone https://github.com/rezasoumi/MachineLearningNotebooks.git
cd machine-learning-projects