https://github.com/lakshya-gg/machine-learning
This repo provides solutions and accompanying notes for the Machine Learning Specialization offered by Stanford University and Deeplearning.ai on Coursera, taught by Professor Andrew Ng, as of the year 2023.
https://github.com/lakshya-gg/machine-learning
anomaly-detection collaborative-filtering linear-regression logistic-regression neural-networks recommender-system regularization reinforcement-learning tensorflow
Last synced: about 2 months ago
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This repo provides solutions and accompanying notes for the Machine Learning Specialization offered by Stanford University and Deeplearning.ai on Coursera, taught by Professor Andrew Ng, as of the year 2023.
- Host: GitHub
- URL: https://github.com/lakshya-gg/machine-learning
- Owner: Lakshya-GG
- License: gpl-3.0
- Created: 2024-03-04T10:05:53.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-20T07:26:49.000Z (over 2 years ago)
- Last Synced: 2025-01-03T21:44:03.359Z (over 1 year ago)
- Topics: anomaly-detection, collaborative-filtering, linear-regression, logistic-regression, neural-networks, recommender-system, regularization, reinforcement-learning, tensorflow
- Language: Jupyter Notebook
- Homepage: https://www.deeplearning.ai/courses/machine-learning-specialization/
- Size: 31.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Machine Learning
## Overview
This repository contains materials and codes for Machine Learning, covers a wide range of topics including supervised learning, advanced learning algorithms, unsupervised learning, recommender systems, and reinforcement learning.
## Tools Used
- Python programming language
- NumPy for numerical computations
- Scikit-learn for machine learning algorithms
- TensorFlow for neural network implementation
- Jupyter Notebooks for interactive development and visualization
## Final Outcome
At the end of this we should be able to:
- Implement supervised learning algorithms for regression and classification tasks
- Understand and apply advanced learning algorithms such as neural networks and decision trees
- Utilize unsupervised learning techniques for clustering and anomaly detection
- Build recommender systems using collaborative filtering and content-based filtering approaches
- Develop reinforcement learning algorithms, as demonstrated by the implementation of Deep Q-Learning for landing the Lunar Lander.
## Lunar Lander Landing with Deep Q-Learning
Involves training an agent using Deep Q-Learning to land a Lunar Lander successfully. After many unsuccessful attempts in learning how to do it, the rover was trained to land correctly on the surface, precisely between the flags as indicators. The final landing achieved after training the agent using appropriate parameters showcases the effectiveness of the Deep Q-Learning algorithm in solving complex tasks.
https://github.com/Lakshya-GG/Machine-Learning-Specialization-Coursera/assets/92517597/6c766d3e-f82d-468e-ae38-23fc18667f82
## Contributing
We welcome contributions from the community! Here are some guidelines to follow:
- Please fork the repository and create a new branch for your contribution.
- Make sure to follow the existing code style and conventions.
- Write clear and concise commit messages.
- Submit a pull request with your changes and a description of what you added or fixed.
## Bug Reports and Feature Requests
If you find a bug or have a feature request, please open an issue on the project's [issue tracker](https://github.com/example/project/issues) with a detailed description.
## Code of Conduct
Please note that this project is released with a [Contributor Code of Conduct](https://github.com/example/project/blob/main/CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.
## License
This game is licensed under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html). Please see the [LICENSE.md](https://github.com/Lakshya-GG/Machine-Learning-Specialization-Coursera/blob/main/LICENSE.md) file for more information.