How fizaan-ali/ml-specialization-course-1-practice works
This is a personal supervised-machine-learning study archive, not a deployed prediction service. A learner opens topic-organized notebooks to work through regression, classification, regularization, and NumPy/plotting exercises; some exercises use the included CSV practice data and reusable Python helpers. The README documents the topic coverage and dataset-loading convention. Notebook contents were not sampled, so links from particular notebooks to particular helpers or datasets are not asserted.
Main entry points: data Practice datasets · regression_module.py Regression helpers · logistic_module.py Logistic helpers · linear_regression Linear regression
Components 9
Notebook lessons
- Linear regression
notebooks/linear_regression - Logistic regression
notebooks/logistic_regression - Regularization
notebooks/regularization - NumPy and plotting
notebooks/numpy_and_plotting
Reusable helpers
- Regression helpers
src/regression_module.py - Multiple regression helpers
src/multiple_regression_helpers.py - Logistic helpers
src/logistic_module.py
Practice data
- Practice datasets
data
Other components
- Learner
Connections 6
- Learner → Linear regression: studies
README.md - Learner → Logistic regression: studies
README.md - Learner → Regularization: studies
README.md - Learner → NumPy and plotting: studies
README.md - Regression helpers → Practice datasets: loads house data
src/regression_module.py - Logistic helpers → Practice datasets: loads CSV data
src/logistic_module.py