nsoojin/coursera-ml-py resource

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1435 stars · 489 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Python re-implementations of the programming assignments from Andrew Ng's Coursera Machine Learning course, originally written in Octave/Matlab. It covers eight exercises spanning linear and logistic regression, neural networks, SVMs, k-means, PCA, anomaly detection, and recommender systems using numpy, scipy, and matplotlib.

Use cases

• redo Andrew Ng's Coursera ML assignments in Python instead of Octave
• practice implementing machine learning algorithms from scratch with numpy
• learn how linear regression and logistic regression work step by step
• study worked examples of neural network forward and backpropagation
• find reference implementations of SVM, k-means, and PCA exercises
• prepare for machine learning coursework with guided exercises

When to choose

• you completed or are taking the Coursera Machine Learning course and want Python versions of the exercises
• you want educational, step-by-step numpy implementations of classic ML algorithms
• you prefer plain ndarray code without numpy.matrix

When to avoid

• you need a production-ready machine learning library
• you want modern course content like the updated deep learning specialization
• you need actively maintained code compatible with the latest Python versions

Facets

learning-resource · maturity maintenance

machine-learning data-science machine-learning education tutorials python cross-platform coursera andrew-ng numpy-exercises course-assignments octave-to-python neural-networks support-vector-machines logistic-regression k-means principal-component-analysis anomaly-detection

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