https://github.com/mdbloice/pythoncourse
Materials for the "Python in Machine Learning" PhD course offered at the Medical University Graz
https://github.com/mdbloice/pythoncourse
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Materials for the "Python in Machine Learning" PhD course offered at the Medical University Graz
- Host: GitHub
- URL: https://github.com/mdbloice/pythoncourse
- Owner: mdbloice
- License: cc-by-sa-4.0
- Created: 2024-01-09T09:36:45.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-01-22T11:01:49.000Z (over 1 year ago)
- Last Synced: 2025-02-15T04:43:44.107Z (3 months ago)
- Size: 42 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]
# Python for Machine Learning and Data Science
Materials for the "Python in Machine Learning" PhD course offered at the Medical University Graz.
## Installing Python & Jupyter
To install Python, follow the instructions on [python.org](https://python.org) - note that the instructions vary by operating system.
Oncee you have installed Python, you can install Jupyter, and all the requirements for this course, by first downloading the `requirements.txt` file from this repository and running:
```sh
pip install -r requirements.txt
```
from the command line. This will install Jupyter and all other requirements that are listed in the `requirements.txt` file.
Alternatively, you can install the requirements by copying and pasting the following in to the command line:
```
pip install ipython jupyter numpy pandas scikit-learn xgboost matplotlib biopython scipy pillow opencv-python seaborn graphviz
```
*Note*: you may need to install the Graphviz binaries in addition to the Python version.
Once you have installed Jupyter, you can run the following to start a local Jupyter server which will open in the default browser:
```sh
jupyter lab
```
If a browser window does not open automatically, you will need to copy the URL printed to the console and open this in a browser. Typically this URL is [http://localhost:8888](http://localhost:8888)
**Note**: it is good practice to create a virtual environment for every project that you create, if you wish to do so, see the Python `venv` command, detailed instructions can be found here: [https://docs.python.org/3/library/venv.html](https://docs.python.org/3/library/venv.html) - however, for the purposes of this course it is not a requirement that you use a virtual environment.
## Online Alternatives
If you cannot install Python locally for whatever reason, you can use Google Colab as an alternative. See [https://colab.research.google.com](https://colab.research.google.com) - you will need a Google account to use this service.
Note that the [modul17.com](https://modul17.com) server does not run outside of the seminar class times.
# Course Contents
The following is a list of the contents of the course, with links to the relevant notebooks where applicable.
- What is Machine Learning?
- Terminology
- Fitting a line to some data
- NumPy
- Sci-Kit Learn
- Machine Learning
- Classification
- Regression
- Supervised vs. Unsupervised
- Clustering
- Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- Image Analysis
- PyTorch
- MedMNIST
- Bioinformatics
# License
[![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa]
[cc-by-sa]: http://creativecommons.org/licenses/by-sa/4.0/
[cc-by-sa-image]: https://licensebuttons.net/l/by-sa/4.0/88x31.png
[cc-by-sa-shield]: https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg
This work is licensed under a
[Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.