iLearnUnSupervise

This repository is part of my machine learning learning journey inspired by the Coursera Machine Learning Specialization. It focuses on unsupervised learning techniques, including anomaly detection, K-means clustering, recommender systems, and reinforcement learning.

Project Structure

The repository is organized into the following directories:

1. Unsupervised-Learning

1.1 Anomaly-Detection

• Project: Anomaly_Detection.ipynb
• Description: Implementation of the anomaly detection algorithm to detect failing servers on a network.

1.2 KMeans

• Project: KMeans.ipynb
• Description: Implementation of the K-means algorithm and its application for image compression.

2. Recommender-Systems

2.1 Collaborative-Filtering-Recommender-Systems

• Project: Collaborative_RecSys.ipynb
• Description: Implementation of collaborative filtering to build a recommender system for movies.

2.2 Content-Based-Filtering

• Project: RecSysNN.ipynb
• Description: Implementation of content-based filtering using a neural network to build a recommender system for movies.

2.3 PCA

• Project: PCA_Visualization.ipynb
• Description: Example of Exploratory Data Analysis using Principal Component Analysis (PCA).

3. Reinforcement-Learning

3.1 DeepQ-Learning

• Project: Lunar_Lander.ipynb
• Description: Training an agent to land a lunar lander safely on a landing pad on the surface of the moon.

Coursera Machine Learning Specialization

This repository is part of my machine learning journey inspired by the Coursera Machine Learning Specialization.

Feel free to explore the projects and learnings shared in each directory!

Contributing

If you'd like to contribute to this repository, follow these steps:

1. Fork the repository.
2. Create a new branch for your changes: git checkout -b feature/new-feature.
3. Make your changes and commit them: git commit -m "Add new feature".
4. Push to your forked repository: git push origin feature/new-feature.
5. Create a pull request to merge your changes into the main repository.