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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

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.

Prerequisites

To work with this repository, you need:

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

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.

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.

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! 🎉

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.