This repository contains implementations of various machine learning algorithms developed while taking a practical machine learning course on the MaktabKhooneh platform.
The purpose of this repository is to practice core machine learning concepts through hands-on experiments using Python and common machine learning libraries.
- Decision Tree
- K-Nearest Neighbors (KNN)
- Logistic Regression
- Support Vector Machine (SVM)
- Linear Regression
- Non-linear regression experiments
- K-Means (partition-based clustering)
- Hierarchical Clustering (Agglomerative)
- DBSCAN (density-based clustering)
Experiments include both synthetic datasets and real datasets to explore the behavior of different clustering algorithms.
- Content-Based Recommendation
- Collaborative Filtering
Movie recommendation experiments are implemented using Netflix-style datasets.
This repository represents a collection of practical exercises and final projects completed during the course.
Various public datasets are used in the notebooks.