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!