# Machine Learning Exercises
This repository contains the Python programming exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/). They are part of the curriculum of the [ML for Data Scientists](https://franziskahorn.de/mlws_scientist.html) and [ML in Practice](https://franziskahorn.de/mlws_practice.html) Workshops.
If you have any questions, please send me an [email](mailto:hey@franziskahorn.de).
Have fun!
### Using Python
The programming exercises are written in Python. If you're unfamiliar with Python, you should work through [this tutorial](https://github.com/cod3licious/python_tutorial) first.
#### Working on your own computer
The [Python tutorial](https://github.com/cod3licious/python_tutorial) includes some notes on how to install Python and Jupyter Notebook on your own computer.
Please make sure you're using Python 3 and all libraries listed in the [`requirements.txt`](/requirements.txt) file are installed and up to date. You can verify this with the [`test_installation.ipynb`](/test_installation.ipynb) notebook.
#### Working in the cloud
##### Using Google Colab
If you have a Google account, you can also run the code in the cloud using Google Colab:
[](https://colab.research.google.com/github/cod3licious/ml_exercises)
While Google Colab already includes most packages that we need, should you require an additional library (e.g., `skorch` for training PyTorch neural networks in notebook 6), you can install a package by executing `!pip install package` in a notebook cell. With Colab, it is also possible to run code on a GPU, but this has to be manually selected.
##### Using MyBinder
If you don't have a Google account, you can also use MyBinder, which does not require you to log in:
[](https://mybinder.org/v2/gh/cod3licious/ml_exercises/main)
However, this will take a while to load and might be very slow or even crash due to insufficient RAM for some of the exercises.