Yes. Every demonstration is written on screen and rebuilt by you against the same dataset, so you see how each change affects the results rather than reading about it. The evaluation work in particular only makes sense if you run it yourself. Keep a notebook environment open while you watch.

Semantic search retrieves results by meaning rather than exact word matching. Text is converted into vectors, and the system returns items whose vectors sit closest to the query's vector, so a search for “laptop bag” can return a “notebook sleeve”. It is what makes search work when the user and the document use different words for the same thing.

An embedding is a list of numbers that represents a piece of text in a way that places similar meanings close together. A sentence about refunds and another about returns end up near each other even with no shared words. Course 1 covers how embeddings are generated, what their dimensions represent, and how to store them.

Sparse retrieval, such as BM25, matches on terms and is precise when exact words matter, including names, codes, and identifiers. Dense retrieval uses embeddings and matches on meaning, which handles paraphrasing. Course 2 builds both on the same dataset and fuses them, because most real systems need each one's strengths.

Hybrid search combines dense and sparse rankings into a single result list. It is the right choice when queries mix conceptual language with exact terms that semantic search tends to blur, which is most product, support, and documentation search. You implement the fusion yourself rather than relying on a library default.

FAISS is an open-source library for storing vectors and searching them quickly, including approximate methods that trade a little recall for a large speed gain. It runs locally with no service to manage, which makes it a good way to learn indexing before moving to a hosted vector database.

Retrieval augmented generation is semantic search with a language model attached to the end. The retrieval half, which is what this Specialization teaches, is where most RAG systems succeed or fail, because a model cannot ground an answer in a passage that was never retrieved.

Reranking reorders the top candidates with a more accurate but slower model, typically a cross-encoder that scores each query and document together. It matters because first-stage retrieval optimizes for speed and recall, which often places the best result outside the top few. Course 3 implements a lightweight reranker and measures the improvement.

With a labeled set of queries and expected results, scored using Precision@k for correctness, Recall@k for coverage, and Mean Reciprocal Rank for how high the first good result appears. Course 3 covers building that set, which is the part most teams skip, and pairs the metrics with qualitative error analysis.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.