AI for Developers: A Practical First Month with LLM APIs
by Priya Raman
From first model to production inference, without the hand-waving.
Practical machine-learning and applied-AI courses for developers who want working systems rather than lecture notes. You learn the maths only where it changes a decision, then build classifiers, recommenders, retrieval pipelines and agents you can actually deploy. Every project runs on a laptop — no cluster required.
Showing 1–10 of 10 courses
Your first month with model APIs: keys, streaming, retries, cost and one shipped feature.
Understand embeddings well enough to build search that finds meaning, not just keywords.
Chunking, hybrid search and reranking, the parts that decide whether retrieval really works.
Pick, index and operate a vector store without turning your search into a science project.
Make assistants a genuine part of your workflow, not a party trick abandoned by Friday.
Build a dataset, run a LoRA fine-tune, and prove it beats prompting for your narrow task.
The maths-light foundation that makes model behaviour, metrics and failures make sense.
Queues, caching, streaming, fallbacks and budgets: the architecture behind a reliable AI feature.
Design agents that plan, remember and stop, instead of looping until the budget is gone.
Twenty-four hours taking you from a first API call to a deployed, evaluated AI product.
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