Intro-to-Data-Science-and-Machine-Learning

Materials for an introductory course in data science and machine learning

Setup

Clone the repository

git clone https://github.com/epickens/Intro-to-Data-Science-and-Machine-Learning.git
cd Intro-to-Data-Science-and-Machine-Learning

Create a Conda environment.

conda env create -f environment.yml

Activate the environment

conda activate introML

Project Structure

Each directory in this project contains a number of files that you should complete on your own. The tests directory contains a series of tests that will allow you to check your work.

Testing Your Work

After setting up the environment, you can run the tests with pytest. To run all available tests, simply run

pytest

To run a specific test use

pytest tests/example_test.py

fundamentals/

The fundamentals module contains several excercises designed to bring you up to speed on Python programming.

tutorials/

In tutorials/ you will find lessons on various topics ranging from the command line, to Python functions, to machine learning.

tests/

tests/ holds the tests associated with each assignment. You do not need to modify the files in this directory.

project/

This directory is under construction. I am going to build it into an interactive interface that allows you to view the fruits of your labor through visualizations of the completed assignments.

Acknowledgements

This repository is inspired by Sasha Rush's fantastic MiniTorch project and the associated Cornell course. I have taken the liberty of using operators.py and the associated tests in the initial commit to this repository. I expect the our projects to diverge in all future commits, but the inspiration will remain nonetheless.