How gourabrajak-cloud/aml_lab works
This repository is a sequence of hands-on applied machine-learning labs, delivered mainly as Jupyter notebooks with companion Python exercises and datasets. The user workflow is to work through regression, optimization, polynomial modeling, and model-evaluation lessons, then explore feature selection and PCA. The graph preserves those documented-by-name curriculum branches and shows internal calls only where sampled source demonstrates them. Notebook contents and most later-lab implementations were not sampled, so their internal workflows are not inferred.
Main entry points: poly_gradient.py Polynomial Descent · bias_variance_tools.py Model Helpers · Lab_3 Simple Regression · Lab_4 Multiple Regression
Components 15
Linear Regression Labs
- Simple Regression
Lab_3 - Multiple Regression
Lab_4 - OLS Implementation
Lab_4/Lab_4_notebook_solved/mlr_ols_student.py
Gradient Descent Labs
- Batch Descent
Lab_5/Lab_5_BGD_MLR_Solved/mlr_gradient_descent.py - Mini-batch Descent
Lab_6/Lab_6_Solved/mlr_gradient_descent_minibatch.py
Polynomial Modeling
- Polynomial Descent
Lab_8/Lab_8_Solved/poly_gradient.py - Polynomial Features
Lab_8/Lab_8_Solved/poly_gradient.py - Polynomial OLS
Lab_8/Lab_8_Solved/poly_ols.py
Model Evaluation
- Bias-Variance Study
Lab_9/Lab_9_Solved/Lab_9_Bias_Variance.ipynb - Model Helpers
Lab_9/Lab_9_Solved/bias_variance_tools.py - Academic Dataset
Lab_9/academic_outcomes.csv
Feature Analysis
- Feature Selection
Lab_10/Lab_10_Feature_Selection/AML_Lab_10_Feature_Selection_Student_TODO.ipynb - PCA Lab
Lab_11/L11_Student_PCA_TODO.ipynb - Further ML Lab
Lab_12/Lab_12_Colab_Notebook_Todo.ipynb
Other components
- Learner
Connections 12
- Learner → Simple Regression: works through
- Learner → Multiple Regression: works through
- Learner → Batch Descent: practices
- Learner → Mini-batch Descent: practices
- Learner → Polynomial Descent: explores
- Learner → Bias-Variance Study: studies
- Learner → Feature Selection: practices
- Learner → PCA Lab: practices
- Learner → Further ML Lab: works through
- Polynomial Descent → Polynomial Features: creates features
Lab_8/Lab_8_Solved/poly_gradient.py - Polynomial Descent → Polynomial Features: uses features
Lab_8/Lab_8_Solved/poly_gradient.py - Model Helpers → Academic Dataset: reads data
Lab_9/Lab_9_Solved/bias_variance_tools.py