WillKoehrsen/machine-learning-project-walkthrough
Machine Learning Project Walkthrough
This project is an educational resource and step-by-step guide for implementing end-to-end machine learning workflows. It provides a structured walkthrough for managing the entire lifecycle of a predictive modeling project, from initial data cleaning and feature engineering to final model training and performance assessment.
The repository utilizes interactive documents to interleave code, data visualizations, and narrative explanations, facilitating a reproducible approach to data science. By following this guided sequence, users can construct and orchestrate pipelines that transform raw data into validated predictive models using real-world datasets.
The workflow covers the core stages of machine learning development, including pipeline-oriented data processing and modular model evaluation. It emphasizes repeatable processes, allowing for iterative experimentation through stateful data persistence and declarative configuration of model parameters.
Features
- End-to-End Training Pipelines - Constructs end-to-end predictive pipelines from raw data ingestion to model evaluation.
- Model Training Pipelines - Develops end-to-end predictive workflows that transform raw data into trained models.
- Data Processing Pipelines - Structures data processing as a sequence of discrete transformation stages for cleaning and feature engineering.
- Data Science Workflows - Manages the end-to-end lifecycle of machine learning projects through repeatable data science workflows.
- Model Performance Evaluators - Implements modular evaluation of model performance using isolated validation sets.
- Predictive Model Development - Provides a guided sequence of operations for developing and validating predictive models.
- Notebook-Based Experimentation - Utilizes interactive notebook environments to interleave code, visualizations, and narrative for reproducible workflows.
- Machine Learning Tutorials - Provides a structured tutorial for building end-to-end predictive workflows.
- Jupyter Notebook Curricula - Delivers a structured learning path for machine learning tasks through interactive Jupyter notebooks.
- Machine Learning Pipelines - Orchestrates structured multi-part workflows for building, training, and validating machine learning models.
