RAG & Vector Databases

A five-week course that dives into the essential concepts and hands-on skills needed to master RAG and apply them in AI engineering for real-world solutions. You will learn to build RAG systems from scratch and explore advanced topics like hybrid search and multimodal retrieval.

Advanced RAG Systems with Vector Databases

Course Description Stay ahead in the world of AI - ML with this completely updated course covering . Machine Learning. Deep Learning. Large Language Models (LL Ms). Retrieval-Augmented Generation (RAG). AI Agents. Explainable AI (XAI). AutoML using Google Vertex AI This is a hands-on course, designed for active learning. You are encouraged to practice along with the trainer during sessions or immediately after each lecture to build real, practical skills.The content is organized into 21 manageable days, allowing you to learn systematically without feeling overwhelmed. Whether you're a beginner or an experienced professional, you can start from the basics or jump straight to the advanced sections that interest you most.The course is taught by an industry veteran and founder of an AI startup, bringing real-world insights and project-based learning to every module.Running successfully for the past three years, the course has been regularly refreshed to reflect the latest advancements — including cutting-edge topics like Explainable AI, AutoML on Google Vertex, RAG pipelines, and AI Agent frameworks.If you’re looking for a complete, modern, and industry-focused AI learning experience — this is your perfect starting point.Enroll today and build the AI expertise the future demands!What you’ll learn:Build a strong foundation in Machine Learning and Deep Learning concepts Understand and fine-tune Large Language Models (LL Ms) for various applications Design and implement Retrieval-Augmented Generation (RAG)</st

This complete Generative AI course takes you from beginner to advanced with hands-on projects, real-world applications, and career-ready skills. You’ll learn the foundations of Generative AI, explore Large Language Models (LL Ms), master frameworks like LangChain, Llama Index, CrewAI, and PydanticAI, and deploy your own AI solutions on the cloud. The course is tailored to equip you with both the knowledge and practical experience required to step into a Generative AI Engineer role.Each section includes quizzes &amp; coding exercises to help you test your knowledge and reinforce your skills.What you’ll learn in each section1. Introduction – Get started with the course, understand what you will learn &amp; set up Python environments (Colab, Jupyter, Py Charm).2. Generative AI – Foundation – Understand AI vs ML vs DL vs GenAI, dive into Large Language Models, and learn the Transformer architecture.3. Accessing LL Ms in Python – Use OpenAI, Gemini, Groq, and Ollama LL Ms, and connect them through LangChain and Llama Index.4. Prompt Engineering – Explore prompt templates, zero-shot, and few-shot prompting to effectively interact with LL Ms.5. Building GenAI Chatbots – Build and deploy chatbots step by step using LangChain, Llama Index, Streamlit UI, and Streamlit Cloud.6. Retrieval-Augmented Generation (RAG) – Understand RAG, build RAG pipelines with LangChain and Llama Index, and create a PDF Q&amp;A bot.7. AI Agents – Learn what AI agents are and build agents with PydanticAI, Auto Gen, and CrewAI for multi-agent workflows.8. LLM Deployment – Deploy open-source LL Ms with Ollama, Docker, and vLLM, and set them up on AWS EC2 for real-world usage.

Step into the world of Generative AI and Large Language Models (LL Ms) with this Complete Generative AI Mastery Course, an immersive, project-driven program designed to take you from fundamentals to professional-level mastery. In this Generative AI course, you will build production-grade AI applications using industry-standard frameworks such as LangChain, LLaMA 3, FAISS, and Milvus — the same technologies powering real-world enterprise and research-grade AI systems.The course begins with a deep dive into the core concepts of Transformers, GANs, embeddings, and foundation models, helping you understand how modern generative models process and generate human-like content. You will then explore Retrieval-Augmented Generation (RAG), vector databases, and multimodal AI to create powerful, context-aware, and intelligent solutions for text, image, and video understanding.Through 12+ guided, hands-on projects, you will build:AI chatbots powered by LL Ms Intelligent document retrieval &amp; RAG systems Image generation &amp; Vision AI applications Semantic similarity search enginesAI-powered video retrieval systems You will work with cutting-edge models and architectures, including T5 and multimodal models, while applying best practices for real-world system design.By the end of this course, you will master the complete Generative AI pipeline — from data ingestion, embeddings, model chaining, fine-tuning, and optimization to scalable deployment across edge, cloud, and hybrid environments.Whether you are a Python developer, AI enthusiast, data scientist, researcher, or tech innovator, this course equips you with the <st

UPDATES NOVEMBER 20252026 Version of the course released with all code up to date.OpenAI Responses Endpoint and GPT-5 implemented across the sections.New no-code RAG with Flowise.New Project with Streamlit.UPDATES JUNE 2025Launched 2 sections:&nbsp;Image Generation with OpenAI and Reasoning ModelsMCP is now live!UPDATES MAY 2025Launch of 2 new sections: RAG with OpenAI File Search and RAGAS Minor video remakes due to mistakes.UPDATES&nbsp;APRIL 2025:Remake of 3 sections: Retrieval Fundamentals, Generative Fundaments and Introduction to RAG Added Knowledge Graphs with Light RAGUPDATES DECEMBER&nbsp;2024:Fine Tuning OpenAI GPT-4o Python Crash Course + Self-assessmentUPDATES NOVEMBER&nbsp;2024:CrewAI and CrewAI Capstone Project launched The section on OpenAI API for Text and Images is live + OpenAI API Capstone ProjectUPDATES OCTOBER 2024:OpenAI Swarm is live Agentic RAG is live Multimodal RAG Project is live Unlock the Power of RAG, AI Agents, and Generative AI with Python and OpenAI in 2026!Welcome to "RAG, AI Agents, and Generative AI with Python and OpenAI 2026"—the ultimate course to master Retrieval-Augmented Generation (RAG), AI Agents, and Generative AI using Python and OpenAI's cutting-edge technologies. If you aspire to become a leader in artificial intelligence, machine learning, and natural language processing, this is the course you've been waiting for!<s

This course leverages the power of both LangChain and Llama Index frameworks, along with OpenAI GPT and Google Gemini AP Is, and Vector Databases like ChromaDB and Pinecone. It is designed to provide you with a comprehensive understanding of building advanced LLM RAG applications through in-depth conceptual learning and hands-on sessions. The course covers essential aspects of LLM RAG apps, exploring components from both frameworks such as Agents, Tools, Chains, Memory, Query Pipelines, Retrievers, and Query Engines in a clear and concise manner. You'll also delve into Language Embeddings and Vector Databases, enabling you to develop efficient semantic search and similarity-based RAG applications. Additionally, the course covers various Prompt Engineering techniques to enhance the efficiency of your RAG applications.List of Projects/Hands-on included: Develop a Conversational Memory Chatbot using downloaded web data and Vector DB Create a CV Upload and Semantic CV Search App Invoice Extraction RAG App Create a Structured Data Analytics App that uses Natural Language Queries Re Act Agent: Create a Calculator App using a Re Act Agent and Tools Document Agent with Dynamic Tools: Create multiple Query Engine Tools dynamically and orchestrate queries through Agents Sequential Query Pipeline: Create Simple Sequential Query PipelinesDAG Pipeline: Develop complex DAG Pipelines Dataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response Synthesizer Working with SQL Databases: Develop SQL Database ingestion Bot Create a FAST&nbsp;API for your LangChain Application just

Master OCR with Python and OpenCV: Become a Computer Vision Expert Unlock the Power of Text Extraction with AI &amp; Generative AI This comprehensive course will equip you with the skills to:Build Cutting-Edge OCR Systems: Go beyond traditional OCR with Python and OpenCV. Learn to leverage the power of Large Language Models (LL Ms) and Retrieval Augmented Generation (RAG) to create intelligent and accurate text extraction systems.Master Deep Learning Techniques: Dive into advanced deep learning models like CTPN and EAST for text detection and recognition.Integrate GenAI for Enhanced OCR: Discover how to integrate Generative AI with LL Ms and RAG to improve OCR accuracy, extract insights from unstructured text, and automate complex document processing tasks.Apply OCR to Real-World Scenarios: Implement OCR solutions for a variety of applications, including document digitization, invoice processing, and more.Stay Ahead of the Curve: Keep up with the latest advancements in OCR, Computer Vision, LL Ms, RAG, and Generative AI.Key Features:Hands-On Projects: Gain practical experience with real-world projects, such as invoice processing, KYC digitization, and business card recognition.Expert Guidance: Learn from experienced instructors who will guide you through every step of the process.In-Depth Coverage: In-Depth Coverage: Explore a wide range of topics, from fundamental image processing and deep learning to advanced LLM and RAG techniques.Dedicated Support: Get 24/7 support from our team of experts.Flexible Le

Updated videos with new and improved slides. Fixed all the voice issues. Hope you like the course and please give feedback!Unlock the Power of Amazon Bedrock to Build AI-Powered Applications Welcome to Mastering Amazon Bedrock, a comprehensive course designed to help you harness the power of AWS Bedrock’s tools and services. Whether you're a beginner or an experienced developer, this course will take you step-by-step through concepts, configurations, and hands-on exercises that showcase the potential of AWS Bedrock in building intelligent applications.What You’ll Learn:Knowledge Bases (KB): Dive deep into the concept of vector embeddings and retrieval-augmented generation (RAG), essential for optimizing large-scale AI applications. Learn how to configure Knowledge Bases and integrate them seamlessly with other AWS Bedrock tools using practical examples to solidify your understanding.RAG with Amazon Bedrock - We will use Anthropic Claude Model with Open Search Serverless as vector storage to perform the RAG operationsRAG with Open Source - We will also use OpenAI's ChatGPT model with in memory vector storage to perform RAG operations Retrievers - RAG pattern relies heavily on retrieval. There are many ways to retrieve data for summarization. We will learn and explore about different ways to retrieve the contents. Followed by a hands-on activity&nbsp; &nbsp;AI&nbsp;Agents: Master the configuration of AWS Bedrock agents to streamline AI workflows. Gain hands-on experience in implementing action groups, handling parameters, and orchestrating requests effectively to Knowledge Bases. Understand how agents serve as the backbone of dynamic and intelligent AI interactions. We will cover 2 use cases of AI Agents. Multimodal Nutritional AI Agent - We

Are you ready to learn how to build powerful and AI-supported chatbots from scratch?there are a lot of courses out there that teach you how to develop chatbots. So what makes this course DIFFERENT?We're NOT going to use any cloud-based chatbot solutions like Dialogflow, IBM Watson, or Microsoft Azure. Instead, we'll be focusing on free and open-source technologies that are just as robust and powerful.We're NOT just going to talk only about the basics of chatbot development. We’re going to dive deeply into this world.This course is full of project-based tutorials. A lot of techniques will be derived via developing a set of chatbot projects Chatbots are everywhere and are becoming an increasingly important part of our daily lives. They're used for a wide range of applications, from customer service to online shopping, and they're only getting more advanced and sophisticated.In the course, we delve into the different types of chatbots and their use cases, including rule-based chatbots, AI-powered chatbots, and conversational AI. We also cover the various technologies and platforms that are used to build chatbots, such as natural language processing (NLP), machine learning (ML), and chatbot development open-source projects like Botpress, Set Fit, GLiNER, Transformers, LangChain, fastAPI, Docker, and more.In this course, you will learn:How to Setup Your Development Environment Tools How to Install and start your first Botpress project You will Understand what the conversation flow studio is Develop the different types of chatbot response templates You will learn how

Yapay Zeka uygulamaları geliştirmek, LangChain'e hakim olmak, Retrieval Augmented Generation örnekleri geliştirmek ve&nbsp; LLM orkestrasyonu&nbsp;konusunda uzmanlaşmak istiyorsanız şu anda doğru yerdesiniz!Bu eğitim ile LangChain'in nasıl çalıştığını, nasıl kullanıldığını, RAG&nbsp;gibi önemli konseptlerin nasıl uygulandığını örnekler yaparak öğrenceksiniz. Eğitim içerisinde anlatılan tüm teknik konular translator, ileri seviye chatbot, ileri seviye RAG gibi uygulamalar yapılarak pekiştirilmektedir. Profesyonel anlamda ortaya uygulamalar koymak için gerekli olan tüm bilgilerin de üstünden geçeceğimiz bu eğitime katılmak için temel programlama bilgisine sahip olmanız gerekmektedir.Kurs Udemy'de 350.000+ öğrenciye Yazılım ve Siber Güvenlik eğitimleri veren ve Boğaziçi Üniversitesi'nde Yazılım Eğitmeni olan Atıl Samancıoğlu tarafından veriliyor!&nbsp;Siz de Yapay Zeka orkestrasyonuna adım atmak istiyorsanız aşağıdaki detaylı açıklamayı inceleyip&nbsp;kursa hemen kayıt olabilirsiniz.Bu kursta LangChain, RAG vb. birçok konuyu beraber işleyeceğiz. Eğitimin içindeki bölümlerde değineceğimiz konulardan bazıları şunlar:LangChain Langgraph Retrieval Augmented Generation Agents Message History, Memory Management Langsmith Langserve Vectorstore ChromaDB OpenAI&nbsp;API Custom GP Tsİçerik &amp;&nbsp;Genel GörünüşBu kurs yapay zeka araçları geliştirmeyi ve özellikle de büyük dil modellerini bu sürece dahil etmeyi düşünenler için idealdir. LLM'lerin LangChain ile nasıl yönetilebileceğini, Langgraph ile Agent'larımızın nasıl koordine edilebileceğini, RAG&nbsp;gibi ileri seviye konuların nasıl basit hale getirileceğini pratik projeler yaparak göreceğiz.Eğitim pratik odaklı olsa da kesinlikle teorik boyutu aksatılmadan en ince detayları işlemeyi ih