Learning Paths

For engineers aiming at frontier AI research. Build math, ML and systems depth, then train, align, scale and evaluate models and publish reproducible results.

For developers who want to build vision systems. Learn image processing, deep learning and 3D vision, then train, optimize and deploy models on cloud and edge hardware.

For beginners and career switchers who want to turn raw data into decisions. Learn spreadsheets, SQL, BI, Python and statistics, then build a portfolio and land an analyst role.

For aspiring data scientists who want to turn raw data into decisions. You will be able to analyze data, build and evaluate ML models, communicate insights, and ship models to production.

For engineers who want to design, train, scale, and deploy deep neural networks. You will be able to build vision, sequence, generative, and LLM systems and run them efficiently in production.

For developers who want to build production LLM systems. You will be able to fine-tune models, build RAG pipelines and agents, evaluate and secure them, and serve them efficiently at scale.

For developers who want to build production ML. Go from Python and math foundations to training, deploying, and monitoring classical, deep learning, and LLM systems end to end.

For engineers who want to take ML and LLM models from notebooks to production. You will build, deploy, monitor, and automate reliable ML systems on cloud infrastructure.

For developers who want to build language AI, from classical text processing to transformers and LLMs. You will train, adapt, evaluate, and deploy production NLP systems.

For developers, analysts, and builders who want reliable results from LLMs. You will design, evaluate, and ship prompts, RAG pipelines, and agents that work in production.

A beginner-to-practitioner path for AI newcomers. Go from your first line of Python to building, deploying, and responsibly shipping ML, deep learning, and LLM systems.