🌟 ML_Course

Welcome to the ML_Course repository! This is a curated collection of machine learning concepts, covering both theoretical insights and practical implementations. Perfect for beginners and practitioners looking to strengthen their knowledge. 🚀

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📚 Table of Contents

1. 📈 Regression Models
2. 🧑‍🏫 Classification Models
3. 🔍 Clustering Models
4. 📉 Dimensionality Reduction
5. 🛒 Association Rule Mining
6. ⏳ Time Series Models
7. 🎥 Recommendation Systems
8. 🖼️ Image Classification (SVM)

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✨ Overview

Explore the topics below, each featuring detailed models with hands-on implementations.

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📈 Regression Models

Predict continuous outcomes with the following techniques:

• Linear Regression
• Polynomial Regression
• ElasticNet Regression
• Decision Tree Regression
• Random Forest Regressor
• Support Vector Regression (SVR)

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🧑‍🏫 Classification Models

Categorize data into discrete classes using the following techniques:

• Logistic Regression
• Decision Tree Classification
• Random Forest Classification
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Naive Bayes Classification

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🔍 Clustering Models

Group similar data points with these unsupervised learning techniques:

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN

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📉 Dimensionality Reduction

Simplify complex datasets using the following techniques:

• Principal Component Analysis (PCA)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• LDA (Linear Discriminant Analysis)
• Independent Component Analysis (ICA)

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🛒 Association Rule Mining

Discover patterns and relationships within datasets using the following techniques:

• Apriori Algorithm
• FP-Growth Algorithm

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⏳ Time Series Models

Analyze and predict sequential data over time using the following techniques:

• Seasonal Decomposition
• ARIMA Model
• SARIMA Model
• Auto ARIMA
• Exponential Smoothing

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🎥 Recommendation Systems

Implement systems to predict user preferences:

• Content-Based Filtering
• Collaborative Filtering
• Hybrid Systems

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🖼️ Image Classification (SVM)

Classify images using Support Vector Machines:

• SVM for Image Classification

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🛠️ Getting Started

Follow these steps to explore the repository:

1. Clone the repository: `bash git clone https://github.com/pradeep-016/ML_Course.git

2. Navigate to the desired section:

cd ML_Course/'Section Name'

3. Run the code: Open Jupyter Notebook files to explore and execute the code.

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⚙️ Prerequisites

Ensure you have the following installed: Python 3.x

Jupyter Notebook

Python Libraries:

numpy

pandas

matplotlib

scikit-learn

scipy

Install all required dependencies:

pip install -r requirements.txt

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🤝 Contributing

Contributions are highly appreciated! ❤️

To contribute:

1. Fork the repository.

2. Create a new branch:

git checkout -b feature/YourFeature

3. Commit your changes:

git commit -m "Add some feature"

4. Push to the branch:

git push origin feature/YourFeature

5. Open a Pull Request.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🙏 Acknowledgments

Special thanks to all contributors and the open-source community for their invaluable resources and support. 💡

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🎯 Let's Learn Together!

If this repository helps you in any way, feel free to ⭐️ the repo and share it with others. Let's make machine learning fun and accessible for everyone!