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Title: Classic ML Algorithms labs in Made

Description:

Welcome to the GitHub repository for the Laboratory Works of Classical Machine Learning Algorithms! This repository serves as a comprehensive resource for understanding and implementing fundamental machine learning algorithms. Here, you'll find a collection of Jupyter notebooks, code examples that guide you through the step-by-step implementation and evaluation of classic ML algorithms.

Project Overview:

In this repository, we dive into the core concepts of classical machine learning, exploring well-known algorithms that form the foundation of modern data analysis and predictive modeling. Each laboratory work is designed to introduce you to a specific algorithm and provide hands-on experience with real-world datasets.

Key Features:

  • Implementation of Classical ML Algorithms: Covering popular algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and more.
  • Data Preprocessing: Techniques to clean, transform, and prepare data for training and testing the ML models.
  • Model Evaluation: Utilizing metrics like accuracy, precision, recall, F1-score, and ROC curves to assess algorithm performance.
  • Hyperparameter Tuning: Exploring methods to optimize model parameters for improved results.
  • Interactive Jupyter Notebooks: Hands-on exercises and practical examples for easy learning and experimentation.

Project Structure:

  • homeworks
    • assignment0_01_knn: Notebook with classical KNN implementation
    • assignment0_02_lin_reg: Notebook with linear regression implementation with regulatization
    • assignment0_03_tree: Notebook with classical decision tree implementation
    • assignment0_04_nn_from_scratch: Notebook of neuron network hand implementation
    • lab01_ml_pipeline:Notebook with sklearn classical ml playground
    • lab02_deep_learning: Notebook with deep learning playground
  • practice_sessions
    • data: folder for storing notebooks data
    • models: folder for storing saved models
    • img : folder for storing images used in notebooks
    • Notebooks.ipynb: notebooks files for various of topics

Getting Started:

  • git clone https://github.com/your-username/classic-ml-algorithms-lab.git
  • Install the required libraries specified in the requirements.txt file in each lab.
  • Access the Jupyter notebooks in the notebooks directory to follow the step-by-step implementations.
  • Modify and experiment with the code to gain a deeper understanding of the algorithms.

Thank you for visiting my repository! I hope these laboratory works provide you with a solid foundation in classical machine learning algorithms and inspire you to explore more advanced concepts in the field. Happy learning!