Vector databases form the backbone of generative AI, using embeddings to capture data meaning and assess vector similarities. They are vital in fields like natural language processing, image recognition, recommender systems, and semantic search. With the rising use of LLMs, vector databases offer real-time proprietary data access, facilitating the creation of Retrieval Augmented Generation (RAG) applications.

Given the importance of vector databases, we have curated a set of courses for your learning journey. 

Vector Databases: From Embeddings to Applications

Andrew Ng-led DeepLearning.AI’s ‘Vector Databases: From Embeddings to Applications‘ is a free course on vector databases, their applications, and how they can be used to develop generative AI applications without training or fine-tuning an LLM. The course aims to help learners gain the knowledge to make informed decisions about when to apply vector databases to their applications. 

The key topics in this course include, using vector databases and LLMs to gain deeper insights into data, building labs that demonstrate how to form embeddings using various search techniques to find similar embeddings and exploring algorithms for fast searches through vast datasets and building applications ranging from RAG to multilingual search.

Master Vector Database with Python for AI & LLM Use Cases

In this course by Udemy, you will learn to work with Vector Databases using Python with a focus on AI and LLM applications. The course covers techniques for vector data embedding, indexing, and retrieval, including practical exercises with semantic search and named entity recognition. You will also explore Pinecone vector database, LangChain, and transformer models for vector embedding, along with generative AI and OpenAI API usage. 

At INR 449, the course provides insights into the fundamentals of vector databases and their role in AI workflows, enabling similarity search and nearest neighbour retrieval. This course is suitable for data professionals, AI researchers, machine learning engineers, and anyone with a technical background interested in cutting-edge AI technologies. 

A basic understanding of programming concepts and proficiency in at least one language, like Python or Java, is essential. Additionally, an understanding of data analysis and machine learning, familiarity with databases, encompassing tables, queries, data manipulation principles, and knowledge of NumPy and Pandas for data manipulation is advantageous. 

Learn Embeddings and Vector Database

The curriculum covers understanding and creating embeddings using vector databases, and implementing advanced AI solutions. The skills one can learn from this free course include proficiency in AI, online databases, AI development, AIOps (AI for IT Operations), and AI systems. With a flexible schedule and an intermediate level, this subscription-based two-hour course requires no previous experience and is designed for self-paced learning.

The course focuses on embeddings and their role in interpretative processes, covering practical exercises with tools like Supabase. Participants engage in challenges in text pairing, semantic searches, and similarity searches, mastering tasks such as creating conversational responses with OpenAI and handling text chunking.

The course combines theoretical understanding with practical skills, ensuring learners not only understand the technical aspects but also develop a proof of concept for an AI chatbot, prepared for real-world challenges.

Master Vector Databases

This course, priced at INR 449, includes seven hours of on-demand video, five articles, and 12 other resources. The instructor emphasises practical learning through code-along exercises, providing skills to build and optimise vector indexing systems for real-world applications. 

It explores the fundamentals of vector databases and their applications in AI, generative AI, and language models. Topics covered include vector basics, embedding techniques, SQLite as a Vector Database, ChromaDB, Pinecone DB, Qdrant Vector Database, and applications of LangChain and OpenAI Embeddings. 

LangChain & Vector Databases in Production

Gen AI 360, a collaboration between Activeloop, Towards AI, and Intel Disruptor Initiative, provides foundational model certification for generative AI professionals, executives, and enthusiasts. This free certification is a comprehensive three-course series that provides essential skills for mastering LLMs, covering everything from training to implementation in production.

The first course, featuring over 50 lessons and 10 practical projects, focuses on LangChain and introduces Deep Lake, a cutting-edge vector database for AI data. It also covers prompt engineering, knowledge organisation with indexes, and building applications such as automated sales agents and recommendation engines.

The course is designed for individuals with intermediate Python knowledge, basic understanding of Jupyter Notebooks, and familiarity with GitHub. 

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