#6565th of 65 in Learning and cookbooks
machine-learning-specialization-coursera
Solved notebooks for Andrew Ng's Coursera Machine Learning Specialization
- Stars
- 8.1k
- License
- MIT
- Last commit
- Dec 2025
- Language
- Jupyter Notebook
7,117 on 10 Apr 20268,142 on 30 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Jupyter notebooks with solutions to the practice quizzes, optional labs and programming assignments of the three-course Machine Learning Specialization on Coursera. Topics run from linear and logistic regression through neural networks in TensorFlow and NumPy, decision trees, clustering, anomaly detection and recommenders. The README is an index of links to each week's material.
Who it is for: Learners taking or reviewing Andrew Ng's Machine Learning Specialization
Strengths
- Covers all three courses week by week, with quizzes, labs and assignments
- Solved notebooks in Python, using NumPy, scikit-learn and TensorFlow
- MIT license, so notebooks can be reused freely
Weaknesses
- Includes graded assignment solutions, which undermines the course exercises
- Tied to one Coursera course, not a general ML reference
- README gives no setup or install instructions; environment is unknown
- Some README links point to the same notebook under different labels
What it needs
- no GPU
- Needs Jupyter, NumPy, scikit-learn, TensorFlow
Quick answers
Is machine-learning-specialization-coursera open source?
Yes. Its code is on GitHub under the MIT license.
Does machine-learning-specialization-coursera need a GPU?
No, it runs without one.
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