python_machine_learning
🧠 Introduction
Resources for teaching machine learning in Python.
📋 Table of Contents
💾 Installation
pip install -r requirements.txt
🧠 Python Machine Learning Teaching Resource
Repository: https://github.com/neelsoumya/python_machine_learning
📦 Installation Instructions (for Google Colab)
1. Clone the repository (optional, for full access)
!git clone https://github.com/neelsoumya/python_machine_learning.git
%cd python_machine_learning
2. Install required Python packages
!pip install -r requirements.txt
or
!pip install numpy pandas keras tensorflow scikit-learn seaborn matplotlib
3. (Optional) Download specific files directly from GitHub
# Example: Download a specific notebook
!wget https://raw.githubusercontent.com/neelsoumya/python_machine_learning/main/PCA_movie_ratings.ipynb
How to use in Colab:
- Open a new notebook in Google Colab (https://colab.research.google.com/).
- Run the above commands in code cells.
- You can now open and run any of the notebooks or scripts from the repository.
-
Open a new notebook in Google Colab
- If you want to run the notebooks in Colab, you can also use the Open in Colab badge below:
-
Run the commands in code cells.
-
You can now create notebooks and run any of the scripts in Google Colab.
Repository link:
https://github.com/neelsoumya/python_machine_learning
📁 Files & Resources
Python Basics
- Free coursera course on Python basics
- Introduction to Python Cambridge Bioinformatics Training Center
- Quick refresher on Python
- Data visualization in R
- calling_functions.py: How to call and create functions in Python
- dictionary.py: Dictionaries in Python
- classses_objects.ipynb: Classes and objects in Python
- 📓 Relative path in Python and loading files
- Coding interviews
Unsupervised Learning
- comprehensive course on unsupervised machine learning
- unsupervised.md: Resources for teaching unsupervised machine learning
- singlcecell_unsupervised.ipynb: Unsupervised ML on single-cell biological data
- EHR_data_unsupervised_learning.ipynb: Unsupervised ML on EHR data
- pca_when_not_to_do.ipynb: When not to apply PCA and how to apply tSNE
- PCA_movie_ratings.ipynb: PCA and tSNE on movie ratings data
- normalising_data.ipynb: Normalizing data
- pca_notes.ipynb: PCA teaching notes
- pca_notes.md: PCA teaching notes (markdown)
Supervised Machine Learning Notebooks
- supervised.md: Resources for teaching supervised machine learning
- Teaching CNNs and neural networks
- teaching material for Part 2 BBS Students basics
- hiring_problem_practice.py: Simulating the hiring problem
- game_of_life.ipynb: Game of Life in Python
- puzzle_number_problem.py: Solving a puzzle from H. Dudeney’s Canterbury’s Puzzles
- data_munging_plotting.py: Data munging and plotting
- gradient_descent.py: Gradient descent explanation
- gradient descent interactive demo
- backprop.py: Backpropagation explanation
Large-language models (LLMs)
PyTorch fundamentals
Basic Statistics
Capstone projects
Open source projects
Big data analytics
Continous integration and pre-commit checks
Git and UNIX fundamentals
Forthcoming
Data & Utilities
data/: Folder containing data filesslides/: Folder containing slides
Other resources
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https://github.com/intro-stat-learning/ISLP_labs/tree/stable
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https://www.statlearning.com/resources-python
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https://www.statlearning.com/
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https://www.youtube.com/playlist?list=PLoROMvodv4rNHU1-iPeDRH-J0cL-CrIda
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Video lectures by the authors of the book Introduction to Statistical Learning in Python
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https://github.com/neelsoumya/public_supervised_machine_learning
-
https://github.com/neelsoumya/public_teaching_unsupervised_learning
Acknowledgements
We thank Martin van Rongen, Vicki Hodgson, Hugo Tavares, Paul Fannon, Matt Castle and the Bioinformatics Facility Training Team for their support and guidance.
📬 Contact
Soumya Banerjee
sb2333@cam.ac.uk