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Each algorithm includes a Jupyter Notebook demonstrating its application and explanation of the concepts.  \n\n## 📁 **Contents**  \n\n- **[1. Supervised Learning](/Supervised_ML/)** — Models like linear regression, svm, knn, decision tree, etc.\n- **[2. Unsupervised Learning](/Unsupervised_ML/)** — Models like Kmeans, etc.\n- **[3. Ensembles](/Ensembles/)** — Models like random forest, gradient boosting, adaboost, etc.\n- **[3. Dimensionally Reduction](/Dimensionally%20Reduction/)** — Models like PCA, LDA, etc.\n\n---\n\n## 🛠️ **Requirements**  \n\nEnsure you have the following libraries installed:  \n\n- Python 3.x  \n- NumPy  \n- Pandas  \n- Scikit-learn  \n- Matplotlib  \n- Jupyter Notebook  \n- TensorFlow  \n- Keras  \n\n---\n\n## 🚀 **Installation**  \n\n1. **Clone the repository:**  \n\n   ```bash\n   git clone https://github.com/Sumit0ubey/Machine_learning.git\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsumit0ubey%2Fmachine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsumit0ubey%2Fmachine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsumit0ubey%2Fmachine_learning/lists"}