ZTM - Complete Machine Learning and Data Science
Table of Contents
- About the Project
- Getting Started
About The Project
The repository contains my code for the Complete Machine Learning and Data Science: Zero to Mastery Udemy course.
You can read my thoughts, lecture notes, and observations on my blog:
- A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 01)
- A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 02)
- A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 03)
The project setup with Docker and docker-compose is heavily influenced by the Data Science Docker Template by Binal Patel.
To get a local copy up and running follow these steps:
You'll need Docker and docker-compose:
docker --version > Docker version 19.03.5-ce
docker-compose -v > docker-compose version 1.25.3
- Clone the repo
git clone https://github.com/sophiabrandt/ZTM-Complete-Machine-Learning-Data-Science.git
.env_devfile with the following format:
# credentials and database information db_username=test_username db_password=test_password db_host=test_host db_port=test_port db_name=test # disables lag in stdout/stderr output PYTHONUNBUFFERED=1 PYTHONDONTWRITEBYTECODE=1 # random seed random_seed=42
- Build and run container
docker-compose up --build -d
Note: The Jupyter Notebook uses Vim key mappings for developent. See Dockerfile-dev. Delete relevevant lines in the Dockerfile if needed.
http://localhost:8888for JupyterLab. Enter access token:
Develop and save any notebooks into
/notebooks. Save final artifacts/models needed for production in
See the open issues for a list of proposed features (and known issues).
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature)
- Commit your Changes (
git commit -m 'Add some AmazingFeature')
- Push to the Branch (
git push origin feature/AmazingFeature)
- Open a Pull Request
Distributed under the MIT License. See
LICENSE for more information.
Original code by Andreie Neagoie, Daniel Bourke. Original Docker setup by Binal Patel.
Sophia Brandt - @hisophiabrandt
- Complete Machine Learning and Data Science: Zero to Mastery by Andrei Neagoie & Daniel Bourke
- Data Science Docker Template by Binal Patel