This repo is a selection of worked examples used in our PhD training.
Biological examples of Python programming, to showcase some example use cases for programming and Python in particular.
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Stats and plotting using an example from enzyme kinetics
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Modelling a zombie apocalypse using ordinary differential equations (ODEs)
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Simulating Michaelis-Menten (catalytic conversion) reactions using PySB
An introduction to the basics of programming in Python, such as importing packages, variables, loops and functions.
You can try out these examples in a free online environment called Binder using the link below. You'll get a Jupyter Lab interface in your browser.
Note that Binder instances are ephemeral - changes are discarded at the end of your session. You can save your work by downloading notebooks to your local computer if desired.
- Learn Git Branching
- An interactive tutorial in the web browser for learning how to use Git version control software.
Free to download as .pdf files. Incredibly important resources for understanding theory and domain of computational modelling.
Foundation in core techniques and fundamental mathematics for machine learning. Start here:
- The Hundred-Page Machine Learning Book - Burkov
- Short and sweet. It gives you a foot in the door with the names and brief overviews of many useful data models.
- Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares - Boyd and Vandenberghe
- Provides a strong background in taking your linear algebra knowledge and expending it to higher dimensional problems.
More advanced books by leading researchers, offering a deeper look at the theory of machine learning: 3. Deep Learning - Goodfellow
- Builds on "Introduction to Applied Linear Algebra" and applies it to models such as MLP and CNN.
- Machine Learning - Bishop
- A more in depth book on image processing methods.
- The Elements of Statistical Learning - Hastie
- An advanced book on Statistical Modelling with many worked examples.
Other interesting topics to learn about:
- Bayesian Methods for Hackers
- A book not about "hacking"! Rather, a book about applying Bayesian statistical modelling to your data.