AI / ML

A practical, connected roadmap from Python and ML foundations to LLMs, RAG, agents, evaluation, production AI, and cloud architecture.

How embeddings represent data as vectors and how similarity search supports retrieval systems.

Evaluate retrieval, generation, safety, reliability, latency, and cost instead of relying on a single model score.

Core security controls for LLM, RAG, and agentic AI applications.

A production-oriented view of problem framing, data, training, evaluation, deployment, monitoring, and iteration.

A practical framework for evaluating LLM, RAG, and agent systems before and after production release.

A practical framework for writing, testing, versioning, and evaluating prompts in production AI systems.

Learn How to learn generative AI easily.

Learn about AI from scratch.

A structured collection of observations, examples, records, or other data used for analysis, training, validation, or evaluation.

Learn about AI from scratch.

Massive data when grouped into a collection called a dataset.

Massive data when grouped into a collection called a dataset.

Massive data when grouped into a collection called a dataset.

Popular and powerful machine learning algorithm that falls under the category of gradient boosting.