VBAF v2.1.0 Complete - Full Machine Learning Framework in Pure PowerShell 5.1

VBAF (Visual Business Automation Framework) is a fully realized machine learning framework built entirely in PowerShell 5.1. It offers a comprehensive suite of ML tools without external dependencies, allowing users to train models directly within PowerShell. The framework includes over 20 modules spanning eight phases, encompassing core foundations to production-ready MLOps features. VBAF's design makes it an effective educational tool by providing readable PowerShell code for each algorithm. Version 2.1.0 covers diverse areas, including supervised, deep, and reinforcement learning, showcasing versatility. It provides comprehensive data preprocessing, feature engineering, and support for various data formats like CSV, JSON, and SQL. The framework integrates advanced features such as a model registry, server for production models, and AutoML capabilities. Explainability tools are included to help understand model behavior and make it more transparent. VBAF also provides extensive resources, including tutorials, project examples, and a troubleshooting guide. The project aims to empower IT professionals familiar with PowerShell to delve into machine learning. Community contributions and feedback are encouraged through the project's GitHub repository.