Lots of room to improve. Start with a README and CI.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Documentation
80
Contributing guidance is in the README, not a dedicated CONTRIBUTING.md (−20 pts).
→ Moving it to a CONTRIBUTING.md makes it easier to find and keeps the README focused. A dedicated file earns +47 pts base.
README is present.
README documents how to install the project.
Licensed under Apache-2.0.
Engineering
22
No tests detected anywhere in the repository.
→ Add automated tests. They prove the code works and give contributors confidence to make changes.
No CI configuration detected in this repository.
→ If your CI lives elsewhere (a private repo that builds this one) or this project is itself a CI/CD tool, mark this check Not Applicable. Otherwise add a GitHub Actions workflow that runs tests on each push. It takes 15 minutes and reassures contributors their changes won't break things.
No linter or formatter config found.
→ Add a linter config such as .eslintrc.json, .prettierrc, ruff.toml, or .golangci.yml to enforce consistent code style.
Lockfile present (requirements.txt). Installs are reproducible.
Issue or PR templates present.
Project health
69
No pushes in over 2 years. Looks unmaintained (−95 pts).
→ A recent commit signals the project is alive and worth using.
.gitignore present.
Dependency manifest found (requirements.txt).
Repository has a description.
Repository health signals
Activity, community, and responsiveness at scan time
Activity
- —Commits (30d / 90d)
- 0Forks
- 0Releases
Community
- —Community health
- —authors own >50% of commits
- 0Watchers
Responsiveness
- —Median issue response
- —Median PR merge time
- 0Open issues
Repository files41 root entries
- .githubGood: Issue or PR templates present.
- datasets
- dockerGood: Environment pinned via docker/Dockerfile.Issue: Build artifacts or local files may be committed (docker/.env) (−40 pts).Fix: Remove them and add to .gitignore.
- images
- .gitignoreGood: .gitignore present.
- 01_the_machine_learning_landscape.ipynb
- 02_end_to_end_machine_learning_project.ipynb
- 03_classification.ipynb
- 04_training_linear_models.ipynb
- 05_support_vector_machines.ipynb
- 06_decision_trees.ipynb
- 07_ensemble_learning_and_random_forests.ipynb
- 08_dimensionality_reduction.ipynb
- 09_unsupervised_learning.ipynb
- 10_neural_nets_with_keras.ipynb
- 11_training_deep_neural_networks.ipynb
- 12_custom_models_and_training_with_tensorflow.ipynb
- 13_loading_and_preprocessing_data.ipynb
- 14_deep_computer_vision_with_cnns.ipynb
- 15_processing_sequences_using_rnns_and_cnns.ipynb
- 16_nlp_with_rnns_and_attention.ipynb
- 17_autoencoders_and_gans.ipynb
- 18_reinforcement_learning.ipynb
- 19_training_and_deploying_at_scale.ipynb
- apt.txt
- book_equations.pdf
- changes_in_2nd_edition.md
- environment.yml
- extra_autodiff.ipynb
- extra_gradient_descent_comparison.ipynb
- index.ipynb
- INSTALL.md
- LICENSEGood: Licensed under Apache-2.0.
- math_differential_calculus.ipynb
- math_linear_algebra.ipynb
- ml-project-checklist.md
- README.mdGood: README is present.Good: README is well structured with multiple sections.Good: README includes screenshots or visuals. Great for first impressions.Issue: README has no code examples (−15 pts).Fix: Show a quick-start snippet so contributors can see what using your project looks like.Good: README links to a live demo or deployed app.Good: README includes status badges.Good: README documents how to install the project.Good: README documents how to run the project.
- requirements.txtGood: Lockfile present (requirements.txt). Installs are reproducible.Good: Dependency manifest found (requirements.txt).
- tools_matplotlib.ipynb
- tools_numpy.ipynb
- tools_pandas.ipynb