This depends mostly on what kind of search engine you're trying to build. I unfortunately won't be able to point you towards courses, but there are tons of grea

This depends mostly on what kind of search engine you're trying to build. I unfortunately won't be able to point you towards courses, but there are tons of great resources online to help you get started. Search engines are a fairly broad topic, and a lot of it depends on the _type of data_ that you want to build a search engine for. If you're looking towards more traditional, Google/Yahoo-like search, Elasticsearch's learning center (https://www.elastic.co/learn https://www.elastic.co/learn) has quite a few good resources that can point you in the right direction. Many enterprise search solutions are built on top of Apache Lucene (including Elasticsearch), and videos/blogs discussing Lucene's architecture (https://www.endava.com/en/blog/Engineering/2021/Elasticsearch-and-apache-lucene-fundamentals-behind-the-relevance-score https://www.endava.com/en/blog/Engineering/2021/Elasticsearc...) is a great starting point as well. Opposite text/web search is _unstructured data_ search, i.e. searching across images, video, audio, etc... based on their semantics (https://www.deepset.ai/blog/semantic-search-with-milvus-knowledge-graph-qa-web-crawlers-and-more https://www.deepset.ai/blog/semantic-search-with-milvus-know...). Work in this space has been ongoing for decades, but an emerging way of doing this is via a _vector database_ (https://frankzliu.com/blog/a-gentle-introduction-to-vector-databases https://frankzliu.com/blog/a-gentle-introduction-to-vector-d...) such as Zilliz Cloud (https://zilliz.com/cloud https://zilliz.com/cloud) or Milvus (https://milvus.io/ https://milvus.io/). The idea here is to turn the type of data that you're searching for into an high-dimensional vector called an embedding, and to perform nearest-neighbor search on the embeddings themselves. Disclaimer: I'm a part of the Zilliz team and Milvus community.