MLX Interview Notes Repository

Welcome to the MLX Interview Notes Repository! This repository is a comprehensive compilation of interview notes, interactive notebooks, and a personal project developed as part of the MLX programme. It brings together detailed content showcasing skills in technology and data science.

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🔍 Overview

This repository serves a dual purpose:

1. Interview Notes:
   Detailed notes with coding examples, Jupyter notebooks, and visual aids covering crucial topics such as:

   • Command Line 💻📂 (to be uploaded soon)
   • Docker 🐋📦 (to be uploaded soon)
   • Git and GitHub 🌳🐙 (to be uploaded soon)
   • Machine Learning 🤖📈
   • Mathematics ➗📊
   • Networking 🌐🔗 (to be uploaded soon)
   • SQL & Postgres 🗄️🐘 (to be uploaded soon)

2. Personal Project – HoopTrax:
   An end-to-end application for NBA Player Performance Prediction and Analysis featuring data processing, model training, interactive visualizations via Streamlit, and containerized deployment with Docker. View the project folder

🚀 Personal Project: HoopTrax

HoopTrax: NBA Performance Prediction & Analysis is an end-to-end demonstration of data science and full-stack application development.

⚙️ Features and Architecture

• Data Processing & Modeling:
  Ingest, preprocess, and merge multiple datasets to derive features and train predictive models (including an EPV model).
• Interactive Web Interface:
  A Streamlit-based app that enables real-time predictions and visualizations.
• Deployment & Logging:
  Containerized using Docker with Docker Compose orchestrating the web app and a PostgreSQL database for prediction logs.

🗃 Database and Documentation

• Database Design:
  Schema and migration files are located in the db directory.
• Documentation:
  Design documents, ERD, and a data dictionary can be found in the Documentation folder within the HoopTrax project.

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⚙️ Getting Started

1. Clone the Repository:

   git clone https://github.com/YuriiOks/MLX-Interview-Notes.git

2. Navigate to the Repository:

   cd MLX-Interview-Notes

3. Explore the Content:
   • Browse the topic folders for detailed interview notes.
   • Examine the personal project within Personal_Project/HoopTrax for a real-world application example.
4. Launch the Personal Project (HoopTrax):
   • Run the Interactive App:

     cd Personal_Project/HoopTrax/Code/streamlit_app
     streamlit run app.py

   • Or Run with Docker:

     cd Personal_Project/HoopTrax
     docker-compose up --build

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🤝 How to Contribute

Contributions are welcome! Here are a few ways you can help:

• Enhance Interview Notes: Enrich topic folders with new examples, clarifications, or additional exercises.
• Improve the Personal Project: Add features, optimize data pipelines, or refine Docker configurations.
• Documentation: Update design documents, the ERD, or the data dictionary as needed.
• Feedback: Open issues or submit pull requests with suggestions and corrections.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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💖 Support the Developer

If you find this project helpful and would like to support its development, consider contributing through one of the following options:

Every contribution, no matter how small, helps and is greatly appreciated! 🙏