/machine-learning-zoomcamp

Personal assignments and solutions for the Machine Learning Zoomcamp course by DataTalks.Club

Primary LanguageJupyter Notebook

Machine Learning Zoomcamp

Overview

This repository contains my assignments and homework solutions for the Machine Learning Zoomcamp 2024 course offered by DataTalks.Club. The course covers a wide range of topics in machine learning, from basic concepts to advanced techniques and deployment strategies. It's a great resource for learning machine learning and applying it in real-world scenarios. Course link: https://courses.datatalks.club/ml-zoomcamp-2024/ .

Course Structure

The Machine Learning Zoomcamp is structured into several modules, each focusing on different aspects of machine learning. The main topics covered include:

  1. Introduction to Machine Learning
  2. Machine Learning for Regression
  3. Machine Learning for Classification
  4. Evaluation Metrics for Classification
  5. Deploying Machine Learning Models
  6. Decision Trees and Ensemble Learning
  7. Neural Networks and Deep Learning
  8. Serverless Deep Learning
  9. Kubernetes and TensorFlow Serving
  10. KServe (optional)

Repository Structure

This repository is organized to mirror the course structure. Each module has its own directory containing:

  • Homework solutions
  • Code snippets and examples
  • Any additional resources or practice exercises

How to Use This Repository

  • Navigate to the specific module directory to find relevant assignments and code.
  • Each homework solution is typically in a Jupyter notebook or Python script format.
  • Additional resources and explanations may be provided in markdown files within each directory.

Prerequisites

To run the code in this repository, you'll need:

  • Python 3.12+
  • Jupyter Notebook
  • Libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, etc. (specific requirements may vary by module)

Setting Up the Environment

  1. Clone this repository:
git clone https://github.com/ShivamKumar2002/machine-learning-zoomcamp.git
  1. Set up a virtual environment (recommended):
python -m venv .venv
source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`
  1. Install required packages:
pip install -r requirements.txt

Disclaimer

This repository is for personal educational purposes. Please ensure you comply with the course's honor code and do not directly copy solutions for your own submissions.

Acknowledgments

  • DataTalks.Club for providing this excellent course
  • All instructors and mentors involved in the Machine Learning Zoomcamp

Additional Resources