How shreelakshmigbhat/ml_practice_notebooks works
This repository is an educational collection of machine-learning notebooks, organized around supervised and unsupervised learning rather than a packaged application. The main workflow is a learner studying a notebook that demonstrates an algorithm with datasets and visualizations. The README documents additional algorithms beyond the sampled source; their exact notebook-level wiring is not established. The sampled SVM GUI separately trains a classifier from local animal images and predicts labels for selected images.
Main entry points: svm_gui.py Cat-Dog SVM GUI · classifications Classification Notebooks · regressions Regression Notebooks · Clustering Clustering Notebooks
Components 13
Supervised Learning
- Classification Notebooks
Supervised Learning/classifications - Regression Notebooks
Supervised Learning/regressions - Classification Data
Supervised Learning/classifications - Regression Data
Supervised Learning/regressions
Unsupervised Learning
- Clustering Notebooks
Unsupervised/Clustering - Clustering Data
Unsupervised/Clustering
Interactive Example
- Cat-Dog SVM GUI
Supervised Learning/classifications/svm_gui.py - Trained SVM
Supervised Learning/classifications/svm_gui.py
Other components
- Learner
- Local Animal Images
- Selected Image
- Cat-Dog Prediction
- Python ML Libraries
Connections 12
- Learner → Classification Notebooks: studies
- Learner → Regression Notebooks: studies
- Learner → Clustering Notebooks: studies
- Classification Notebooks → Classification Data: uses examples
- Regression Notebooks → Regression Data: uses examples
- Clustering Notebooks → Clustering Data: uses examples
- Local Animal Images → Cat-Dog SVM GUI: loads for training
- Cat-Dog SVM GUI → Trained SVM: trains
- Python ML Libraries → Cat-Dog SVM GUI: provides SVM
- Cat-Dog SVM GUI → Selected Image: opens image
- Cat-Dog SVM GUI → Trained SVM: predicts with
- Trained SVM → Cat-Dog Prediction: returns label