ZTM - Complete Machine Learning and Data Science

Code repository for Udemy course
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Table of Contents

• About the Project
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact
• Acknowledgements

About The Project

The repository contains my code for the Complete Machine Learning and Data Science: Zero to Mastery Udemy course.

See the original course materials by Daniel Bourke.

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)
• A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 04)
• A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 05)
• A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 06)
• A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 07)
• A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 08)

The project setup with Docker and docker-compose is heavily influenced by the Data Science Docker Template by Binal Patel.

Built With

• Docker and docker-compose
• Python
• Jupyter Notebooks

Getting Started

To get a local copy up and running follow these steps:

Prerequisites

You'll need Docker and docker-compose:

• Docker

docker --version
> Docker version 19.03.5-ce

• docker-compose

docker-compose -v
> docker-compose version 1.25.3

Installation

1. Clone the repo

git clone https://github.com/sophiabrandt/ZTM-Complete-Machine-Learning-Data-Science.git

2. Create .env_dev file 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

3. Build and run container

docker-compose up --build -d

Usage

Note: The Jupyter Notebook uses Vim key mappings for development. See Dockerfile-dev. Delete relevant lines in the Dockerfile if needed.

1. Go to http://localhost:8888 for JupyterLab. Enter access token: local_dev.

2. Develop and save any notebooks into /notebooks. Save final artifacts/models needed for production in /code.

3. Save final version of code and any models the code relies upon into /code.

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.
Original code by Andreie Neagoie, Daniel Bourke. Original Docker setup by Binal Patel.

Contact

Sophia Brandt - @hisophiabrandt

Project Link: https://github.com/sophiabrandt/ZTM-Complete-Machine-Learning-Data-Science

Acknowledgements

• Complete Machine Learning and Data Science: Zero to Mastery by Andrei Neagoie & Daniel Bourke
• Data Science Docker Template by Binal Patel