Vector databases are the engines behind AI applications. Companies investing heavily in AI need expertise to build AI-powered technologies such as recommendation engines, search engine information retrieval, machine learning tasks, data analysis, semantic matching, and content generation.

This ongoing growth and increasing demand for novel uses of AI-powered applications means that the need for data professionals with vector database skills will continue to grow.

This Vector Database Fundamentals Specialization provides application developers, data scientists, and other AI professionals with valuable vector database skills for building real-world applications such as recommendation engines, personalized user experiences, and other new AI-powered technologies.

Acquire these in-demand vector database skills in this specialization using Chroma DB, MongoDB, PostgreSQL, and Cassandra. You'll perform vector database tasks such as creating embeddings and collections, plus similarity searches, including the computation of similarity scores between query embeddings and document embeddings. You'll gain practical skills through hands-on labs. And you‘ll complete a capstone project where you’ll put your new skills into practice and incorporate RAG and LangChain to solve a real-world business problem using vector data.

Great experience for interviews and your resume! Enroll today and future-proof your AI and data career with the vector database skills businesses need.

应用的学习项目

This Specialization emphasizes applied learning and includes hands-on activities and projects you can talk about with colleagues and in interviews. In these exercises, you’ll take the theory and skills you’ve gained and practice them with real-world scenarios.

Projects include:

• Setting up environments for vector database operations and performing day-to-day database tasks using Chroma DB.

• Storing, indexing, and querying data, including performing vector text similarity searches, and building recommendation systems using MongoDB and Cassandra.

• Applying efficient vector storage, retrieval, and search optimization techniques in PostgreSQL.

• Creating a robust real-world application that uses vector data to solve a business problem.