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Jupyter notebooks for part 1 of the course "Machine Learning in Finance with Python" (ECON5130) taught at Glasgow University
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Author: Richard Foltyn, University of Glasgow
This introductory course consists of several units. Each unit corresponds to one interactive Jupyter notebook, which is also available as a static PDF file. Alternatively, you can download the entire course as a single PDF.
| Unit | Title | Google Colab | |
|---|---|---|---|
| 1 | Language and NumPy basics |  | |
| 2 | Control flow and list comprehensions | | |
| 3 | Reusing code - Functions, modules and packages | | |
| 4 | Plotting | | |
| 5 | Advanced NumPy | | |
| 6 | Handling data with pandas | | |
| 7 | Data input and output |
Detailed slides on how to set up your working environment are available here.
This material is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License,
except for the data files contained in the data/ folder, which
fall under the terms imposed by the original content creators.
Special thanks go to Jonna Olsson for reading through all units and suggesting various improvements.
Selected from shared topics, language and repository description—not editorial ratings.
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Jupyter notebooks for introductory Python course for the MSc program "Data Analytics for Economics and Finance" at the University of Glasgow.
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| 9 | Introduction to unsupervised learning | |
| 10 | Introduction to supervised learning | |
| 11 | Error handling (optional) | |