https://github.com/pravincoder/machine-learning-models-tutorial
https://github.com/pravincoder/machine-learning-models-tutorial
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/pravincoder/machine-learning-models-tutorial
- Owner: pravincoder
- Created: 2024-12-20T10:16:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-10T04:39:17.000Z (over 1 year ago)
- Last Synced: 2025-02-13T17:16:45.940Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 20 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
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README
# 📚 Machine Learning Models Tutorial
Welcome to the **Machine Learning Models Tutorial** repository! 🚀 This project is designed to help you understand, implement, and experiment with a variety of machine learning models. From foundational mathematical models to advanced deep learning architectures, we’ve got you covered!
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## 🧠 What's Inside?
This tutorial is a comprehensive guide to the following categories of models:
### 🔢 Regression Models
- Linear Regression
- Polynomial Regression
- Ridge and Lasso Regression
### 🔍 Classification Models
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
### 📊 Clustering Models
- K-Means Clustering
- DBSCAN
- Hierarchical Clustering
### 🤖 Deep Learning Models
- Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transformers and Attention Mechanisms
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## 💻 How to Use
All tutorials are provided as **Google Colab Notebooks**, making it easy to run, modify, and experiment without worrying about setup hassles.
### Steps:
1. Clone this repository or download the specific notebook you want to explore.
2. Open the notebook in Google Colab ([Colab Link](https://colab.research.google.com/)).
3. Run the cells step-by-step and enjoy learning!
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## 🛠️ Features
- 📈 **Regression, Classification, and Clustering Models**: Learn fundamental and advanced concepts.
- 🧮 **Mathematical Models**: Dive into the theory behind the algorithms.
- 🧑💻 **Deep Learning Models**: Explore modern architectures with practical implementations.
- 📂 **Structured Code**: Well-documented and beginner-friendly.
- 📊 **Visualizations**: Detailed plots and graphs to help you understand results better.
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## ✉️ Maintainer
**Pravin Coder**
📧 Email: [PravinCoder@gmail.com](mailto:PravinCoder@gmail.com)
Feel free to reach out for questions, suggestions, or collaboration opportunities!
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## 📝 License
This project is open-source and available under the [MIT License](LICENSE).
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## 🌟 Contributing
Contributions are welcome! Please feel free to fork this repository and submit pull requests.
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## 🚀 Let's Get Started!
Click the button below to explore the notebooks and start your machine learning journey:
[](https://colab.research.google.com/)
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Happy Learning! 🎉