#Model Evaluation

BA should not evaluate AI by intuition like "the output looks okay". You need a clear protocol: evaluation criteria, scoring rubric, blind test methodology, and a go/no-go framework. A full guide from test set design to sign-off decisions.

Duy TranMay 5, 202614 min

Machine Learning roadmap for beginners starts from zero, following an easy-to-understand method: intuition first, code later, just enough math. The course goes from environment setup, first model, proper evaluation, overfitting/data leakage prevention, to common models (Linear, Logistic, Tree, XGBoost), unsupervised, time series, and production deployment. Each cluster of lessons has mini-projects, real-life challenges and a clear output checklist.

Comprehensive course on Fine-tuning Large Language Models — when to fine-tune, data preparation, fine-tuning on Google Gemini/Vertex AI, OpenAI, and open-source (LoRA/QLoRA). Compare Fine-tuning vs RAG, model evaluation methods, and production deployment. Calculate actual costs.