Ciencia de datos e IA — analítica y bases de ML

Explore, summarize, and visualize small datasets step by step.

Mean, variance, distributions, and practical statistical intuition.

Transform raw data into model-ready features with stable pipelines.

Train, validate, and compare classical machine learning models.

Diagnose model errors and improve decision quality.

Neural network pipelines, optimization, and practical tuning.

Experiment tracking, model versioning, and deployment safety.

Advanced applied tracks for sequential and language data.

Design reliable AI systems with performance and governance controls.

Production workflows for retrieval-augmented generation systems.

Bias control, model safety, monitoring, and compliance.