How alimuhammad78/machine-learning-complete-journey works
This repository is a machine-learning study collection, not a packaged runtime application. Its principal learning workflow moves from preparing data through supervised modeling, evaluation, and model improvement; separate branches hold coursework and reference material. The README documents these topics and their intended workflow, but no source excerpts were provided, so the graph represents documented learning relationships rather than verified notebook execution or function calls.
Main entry points: all_models.ipynb Supervised overview · data_preprocessing Preprocessing techniques · titanic_complete.csv Titanic dataset · Regression Regression models
Components 17
Data preparation
- Titanic dataset
data_preprocessing/titanic_complete.csv - Preprocessing techniques
data_preprocessing
Supervised learning
- Supervised overview
Supervised/all_models.ipynb - Regression models
Supervised/Regression - Regression metrics
Supervised/Regression/Regression_Evaluation_metrics.ipynb - Classification models
Supervised/Classification - Classification algorithms
Supervised/common - Ensemble learning
Supervised/common/Bagging_And_Boosting_Algorithm.ipynb
Evaluation and improvement
- Model evaluation
Supervised/common/Cross_Validation.ipynb - Hyperparameter tuning
Supervised/common/HyperParameter_Tuning.ipynb - Best model selection
Supervised/common/Best_Model_With_Hyperparameter_Tuning.ipynb - ML pipelines
Supervised/common/Machine_Learning_Pipeline.ipynb - Model improvement
Supervised/Regression/Model_Improvement_Techniques.ipynb
Coursework and references
- Course assignments
Assignments - Course slides
slides - Advanced topics
slides/010-Embeddings-SequenceModels-Transformers-Complete-v4.0.pdf
Other components
- Learner
Connections 18
- Learner → Titanic dataset: studies data
- Learner → Preprocessing techniques: studies preparation
- Titanic dataset → Preprocessing techniques: prepares data
- Learner → Supervised overview: studies models
- Preprocessing techniques → Supervised overview: prepares for
- Supervised overview → Regression models: covers regression
- Supervised overview → Classification models: covers classification
- Classification models → Classification algorithms: covers algorithms
- Classification algorithms → Ensemble learning: includes ensembles
- Regression models → Regression metrics: evaluates regression
- Supervised overview → Model evaluation: covers evaluation
- Model evaluation → Hyperparameter tuning: informs tuning
- Hyperparameter tuning → Best model selection: supports selection
- Best model selection → ML pipelines: supports workflow
- Regression metrics → Model improvement: informs improvement
- Learner → Course assignments: completes coursework
- Learner → Course slides: studies references
- Course slides → Advanced topics: covers topics