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Learn RAG & Vector Databases
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Best courses to learn RAG & Vector Databases
Databricks ML in Action
This course offers practical skills to build and deploy machine learning solutions on the Databricks platform, covering the entire ML lifecycle from data ingestion to model deployment. You’ll gain hands-on experience with key tools such as MLflow, Vector Search, and AutoML, while mastering the Databricks Lakehouse architecture. This course will equip you with real-world skills to tackle data science challenges using Databricks' state-of-the-art technologies. The course guides learners through hands-on projects that include tasks like streaming, forecasting, image classification, and retrieval
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Natural Language Processing with Classification and Vector Spaces
In Course 1 of the Natural Language Processing Specialization, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search. By the end of this Specialization, you will have designed NLP applications
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Building LLM Applications with Tools and Retrieval
Building an LLM application requires more than a single prompt: it means calling model APIs, controlling output, connecting tools, and grounding answers in your own data. This course shows you how to build LLM applications in Python that use tool calling and retrieval-augmented generation (RAG). Through follow-along demos, you will call multiple LLM providers from one codebase, compose chains in LangChain, index documents with LlamaIndex, and store embeddings in ChromaDB, Pinecone, and Weaviate. By the end of this course, you will be able to: • Integrate LLM provider APIs and estimate toke
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Building and Fine-Tuning LLM Applications
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you'll learn how to build and fine-tune large language models (LLMs) for real-world applications. Starting with fundamental concepts, you'll progress through hands-on projects that focus on document-based retrieval-augmented generation (RAG) systems, LangChain integration, and fine-tuning techniques. You'll gain the skills to build custom ap
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HelixDB From Zero
Stop stitching three databases together. HelixDB is a Rust-native graph plus vector engine that holds your nodes, your embeddings, your typed edges, and your key-value documents in one process — no separate Postgres, no separate Qdrant, no separate Neo4j. This course walks a Rust-fluent engineer from helix init through a typed HelixQL schema, the helix check and helix compile pre-deploy gates, side-by-side graph traversal and vector search in the same query language, and a typed Rust client that calls a live HelixDB instance with four runtime contracts. Every primitive you meet is wired into a
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Understand RAG Basics
"Understand RAG Basics" is an intermediate course for developers and data scientists who want to build more powerful and trustworthy AI applications. While Large Language Models (LLMs) are revolutionary, they often lack specific, up-to-date knowledge and can hallucinate answers. This 2-hour course provides the fundamental solution: Retrieval-Augmented Generation (RAG). You will need to be familiar with basic Python, API, and LLMs. You will also need Python and a code editor like VS Code installed locally. Focused on practical application, this course transitions from theory to execution. Y
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Fundamentals of Natural Language Processing
The field of natural language processing (NLP) aims at getting computers to perform useful and interesting tasks with human language. This course introduces students to the 3 pillars underlying modern NLP: probabilistic language models, simple neural networks with a focus on gradient based learning, and vector-based meaning representations in the form of word embeddings. At the end of the course, students will be able to implement and analyze probabilistic language models based on N-grams, text classifiers using logistic regression and gradient-based learning, and vector-based approaches to wo
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Deep Learning: Advanced Backbones and Efficient GPU Training
Master advanced deep learning architectures and efficient training techniques using PyTorch Lightning, timm, ConvNeXt, Vision Transformers, RoPE, SwiGLU, RMSNorm, and Weights & Biases. This course equips you to design, train, and benchmark modern backbones on limited GPU hardware for real-world production use. Module 1 introduces modern backbone architectures, tracing the evolution from ResNets to ConvNeXt and Vision Transformers, covering patch embeddings, multi-head self-attention, and position encodings. Module 2 dives into training dynamics and stabilization techniques including RMSNorm,
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Introduction to Vertex AI Embeddings: Text and Multimodal
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will explore the Vertex AI Embeddings API for both Text and Multimodal (Images and Video) use cases.
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AI Systems Design: RAG Pipelines and LLM Architecture
Design and prototype enterprise-grade AI solutions that create real business value. In this course, you’ll learn to distinguish when to use predictive ML versus generative AI, align AI initiatives with business outcomes, and define success criteria that balance accuracy, latency, safety, and cost. You’ll compare traditional deterministic software with probabilistic AI systems to understand where AI is appropriate—and where it isn’t. You’ll diagram modern AI system architectures and evaluate build-versus-buy decisions for key components such as models, vector databases, and orchestration layer
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Weaviate Database Mastery
This intensive course delivers end‑to‑end expertise with Weaviate, the open‑source, production‑grade vector database built for enterprise-scale and AI‑driven search. Beginning with Docker deployment, you will design flexible schemas, index heterogeneous data, and secure clusters with TLS and role‑based access. Hands‑on labs cover GraphQL and REST querying, hybrid keyword‑vector search, and multimodal pipelines that index text and images. Performance modules teach index tuning, sharding, and auto‑scaling to meet low‑latency SLAs. By the final project, you will have built a full‑stack search sol
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Create Chatbots & NLP Apps
Ready to transform customer interactions through intelligent conversation? This Short Course was created to help data analysts and professionals accomplish the development of sophisticated chatbot applications with natural language processing capabilities. By completing this course, you'll be able to implement retrieval-augmented generation systems, optimize conversational flows, extract meaningful insights from unstructured text, and make data-driven decisions about text representation methods. By the end of this course, you will be able to: Build a chatbot prototype using RAG (retrieval-aug
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Advanced RAG Patterns
Advance RAG Patterns is an intermediate course designed for AI developers and ML engineers who have built a basic RAG pipeline but find it still fails on complex or nuanced queries. While foundational RAG reduces hallucinations, production-grade AI demands greater reliability, accuracy, and reasoning. This 2-hour course moves beyond the basics to teach you how to engineer robust, intelligent, and self-correcting systems. Focused on practical, job-ready skills, this course dives deep into cutting-edge architecture. You will learn to implement and evaluate a suite of advanced patterns, includin
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