Machine Learning Tasks

Welcome to the Machine Learning Tasks repository! This repository contains a collection of hands-on tasks implemented during my journey in learning and applying Machine Learning. Each task demonstrates a specific concept, algorithm, or problem-solving approach in Machine Learning.

Table of Contents

• Overview
• Technologies Used
• Prerequisites
• Task Categories
• Contributing
• License

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Overview

This repository showcases various tasks ranging from foundational concepts to advanced techniques in Machine Learning. The tasks are structured to be beginner-friendly, yet comprehensive enough to demonstrate real-world applications.

Goals:

• Strengthen understanding of Machine Learning concepts.
• Implement popular algorithms from scratch and using libraries.
• Work with datasets of varying complexity.
• Analyze results and improve models through experimentation.

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Prerequisites

To work with this repository, you need:

• Python 3.11 or higher
• Jupyter Notebook or JupyterLab
• Basic understanding of Machine Learning

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Technologies Used

The following tools and libraries were used in this repository:

• Python: Programming language for implementing Machine Learning tasks.
• NumPy: For numerical computations.
• Pandas: For data manipulation and analysis.
• Matplotlib/Seaborn: For data visualization.
• Scikit-Learn: For implementing Machine Learning models.
• Jupyter Notebook: For documenting and running code interactively.

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Task Categories

1. Supervised Learning

   • Regression (Linear Regression, Logistic Regression, etc.)
   • Classification (Naive Bayes, Decision Trees, SVM, etc.)

2. Unsupervised Learning

   • Clustering (K-Means, DBSCAN, etc.)

3. Implementation from Scratch

   • Custom implementations of algorithms like Gradient Descent, Logistic Regression, and Naive Bayes.

4. Using Built-in Libraries

   • Applying Scikit-Learn to solve Machine Learning problems efficiently.

5. Regularization

   • Lasso and Ridge Regression with different regularization parameters.

6. Visualization

   • Graphs and plots to analyze data and model performance.

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Contributing

• Contributions are welcome! If you have suggestions or additional tasks to add, feel free to open an issue or submit a pull request. Let's learn and grow together! 🎉

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License

• This repository is licensed under the MIT License. You are free to use and modify the code, but please give credit by linking back to this repository.

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