Advanced LangChain Production

Learn Functions, Tools and Agents with LangChain

Learn Transformers, explained: Understand the model behind GPT, BERT, and T5

State-of-the-Art Machine Learning Papers Implementation

Complete natural language processing specialization covering transformers, attention mechanisms, and modern NLP techniques.

A comprehensive course covering a wide range of topics from the basics of RAG to advanced techniques like fine-tuning and RAG agents. It includes building a basic RAG pipeline, advanced retrieval methods, and optimizing for production.

Discover the power of building AI-powered applications entirely in Kotlin with our comprehensive course on Jetpack Compose, Langchain4j, and Ollama (Local LLM). Whether you're a seasoned developer or a newcomer, this course equips you with the skills to harness these cutting-edge technologies and stay relevant in this AI driven industry. Throughout the course, we focus on practical hands-on learning, guiding you through the creation of six distinct applications leveraging Langchain4j:Hello World AI: Create an AI that greets users dynamically.Rephaser AI: Transform sentences into various tones for easy clipboard use.Unlost AI: Develop an AI to help users remember where they've placed their belongings.Text Adventure AI: Construct interactive storytelling experiences using AI.Resume QnA AI: Utilize AI to summarize resumes and retrieve candidate information interactively.RAG Medium Articles AI: Build a Retrieval-Augmented Generation (RAG) AI for summarizing and querying public Medium articles.You'll gain proficiency in Jetpack Compose for designing modern, responsive UIs and harness Langchain4j and Ollama for AI model management and natural language processing tasks. Learn essential Kotlin programming techniques to seamlessly integrate these technologies and optimize performance on local platforms.Join us to unlock the potential of Kotlin-based AI app development with practical skills and real-world applications in demand today.

Embark on a comprehensive journey into the world of AI agents with Lang Graph. This course is designed to guide you from fundamental concepts to advanced techniques, equipping you with the skills to build sophisticated AI systems. Starting with the core principles, you'll learn about graphs, nodes, edges, and states, and see how they form the foundation of Lang Graph workflows. The course begins with constructing a basic agent, allowing you to grasp the essentials through hands-on practice.Next, you'll dive deeper by building a News Writer Agent, enhancing your understanding by integrating state and tools into your agents. The focus will be on practical applications, ensuring you can visualize and test your agents effectively. Finally, the course introduces advanced techniques, including reflection, human-in-the-loop processes, checkpointers, and threads. You'll also learn to incorporate custom tools, adding versatility and functionality to your agents. Whether you're a beginner or looking to advance your skills, this course provides a structured, step-by-step approach to mastering AI agent development with Lang Graph.The goal of this course is to equip you with the understanding and skills you need to build your own agents. There are plenty of off-the-shelf agents available via Lang Graph and other resources. However, in our experience, when building agents for production you will need to be able to customize. At the end of this course, it is our goal to make sure that you are capable of building your own custom workflows in Lang Graph.Note: Prior python programming experience and some experience with LangChain are required for this course.

In this course, you will dive deep into the world of Generative AI with LL Ms (Large Language Models), exploring the potential of combining LangChain with Python. You will implement proprietary solutions (like ChatGPT) and modern open-source models like Llama and Phi. Through practical, real-world projects, you'll develop innovative applications, including a custom virtual assistant and a chatbot that interacts with documents and videos. We'll explore advanced techniques such as RAG and agents, and use tools like Streamlit to create intuitive interfaces. You'll learn how to use these technologies for free in Google Colab and also how to run projects locally.In the introduction, you’ll be introduced to the theory of Large Language Models (LL Ms) and their fundamental concepts. Additionally, we’ll explore the Hugging Face ecosystem, which offers modern solutions for Natural Language Processing (NLP). You'll learn to implement LL Ms using both the Hugging Face pipeline and the LangChain library, understanding the advantages of each approach.The second part is focused on mastering LangChain. You'll learn to access open-source models, like Meta's Llama and Microsoft’s Phi, as well as proprietary LL Ms, like OpenAI's ChatGPT. We'll explain model quantization to enhance performance and scalability. Key LangChain components, such as chains, templates, and tools, will be presented, along with how to use them to develop robust NLP solutions. Prompt engineering techniques will be covered to help you achieve more accurate results. The concept of RAG (Retrieval-Augmented Generation) will be explored, including information storage and retrieval processes. You’ll learn to implement vector stores and understand the importance of embeddings and how to use them effectively. We’ll also demonstrate how to use RAG to interact with PDF documents and web pages. Additionally, you'll have the opportunity to explore integrating agents and tools, like using LL Ms to perform web searches and retrie

Master the art of building professional-grade Generative AI applications with this comprehensive course designed for advanced developers, data scientists, AI enthusiasts, and technology leaders. This program covers everything you need to know about leveraging Large Language Models (LL Ms) to create robust, scalable, and production-ready AI-powered solutions. Whether you're looking to enhance your skills or build innovative applications, this course is your gateway to success in the AI-driven future.Start with an in-depth exploration of foundational concepts, including the architecture of Generative AI systems, key components, and tools. Learn about advanced topics such as Retrieval-Augmented Generation (RAG), LangChain, Llama Index, and the integration of cutting-edge orchestration frameworks. Gain hands-on experience with cloud platforms like AWS Bedrock, Google Vertex AI, and others to fine-tune your applications and deploy them in real-world scenarios.This course also delves into practical implementations, including chatbots with memory, advanced data retrieval, sentiment analysis tools, and multimodal AI applications. You'll master essential techniques like managing custom data, creating efficient pipelines, and optimizing performance for scalability. By the end of the course, you'll have the expertise to design, deploy, and maintain production-level AI systems that exceed professional standards, empowering you to lead in the rapidly evolving field of Generative AI development and innovation.

This hands-on course teaches you how to build professional level Generative AI Application, intelligent, autonomous AI Agents using MCP (Model Context Protocol) and modern LLM frameworks.Whether you’re an AI beginner or an experienced developer, this course will take you step-by-step through the tools, strategies, and architectures that power modern GenAI applications.What You’ll Learn:- Introduction to Generative AI and its role in modern development- Introduction to Large Language Models (LL Ms) and how they power intelligent applications- Generative AI Architecture Basics – understand the core components of a Gen AI application- Advanced Gen AI Application Architecture for scalable and modular systems- How to apply the Retrieval-Augmented Generation (RAG) technique for enhanced responses- Choosing the Right Orchestration Framework for building LLM-powered apps- LangChain – A modern framework for LLM orchestration- LangChain Expression Language (LCEL) – Build AI flows with clean, declarative syntax- Deep dive into the LangChain Ecosystem for agents, tools, memory, and chains- Mastering Prompt Engineering – Learn to craft optimal prompts for LL Ms- Level 1 Gen AI Applications – Basic AI-powered tools and assistants- Llama Index – An alternative to LangChain for RAG and LLM app orchestration- LLM Ops (Large Language Model Operations) – Manage and monitor LLM Apps- Level 2 Gen AI Applications – Build intermediate systems with memory, tools, and retrieval- Develop Multimodal Gen AI Applications (text, image, audio integration)- Build and deploy AI Agents & Multi-Agent Systems using orchestration frameworks- Level 3 (Professional) Gen AI Applications – Real-time, scalable, production-ready systems- CI/CD for Gen AI – Deploy your Gen AI apps with automated pipelines- Understand and implement MCP (Model Context Protocol) - Ha

Prompt Engineering & LLM Production Master the practical craft of prompt engineering and learn how to design, test, and deploy reliable AI-driven workflows that power real products. This immersive, hands-on course walks you from first principles to production-ready systems, with a focus on reproducible practices, measurable improvements, and real-world integrations. Whether you want to build smarter content pipelines, automated customer support, or code-generation assistants, this course teaches the exact skills, patterns, and guardrails you’ll use every day as an AI prompt engineering practitioner.What this course is (straight, no fluff)This is a pragmatic, exercise-first course on prompt engineering for people who want results — not just theory. You’ll learn how to craft prompts that produce consistent outputs, control model behavior (temperature, top_p, tokens, penalties), evaluate and A/B-test prompt variants, chain prompts into multi-step pipelines, and move from manual experimentation into reliable automation using AP Is and tooling like LangChain and Prompt Layer. The course emphasizes safety, cost-efficiency, and measurable outcomes so you can deploy prompt-based features in production with confidence.Key skills you’ll walk away with Expert-level chatgpt prompt engineering techniques: system/user/assistant role design, few-shot teaching, and format enforcement.Robust experiment practices: hypothesis design, A/B testing, logging, and quantitative metrics (accuracy, F1 proxies, user satisfaction).Production patterns: prompt chaining, map-reduce strategies, validation layers, caching, and failover/human-in-the-loop design.Cost & performance optimization: token compression, reuse strategies, and measurable latency/cost tradeoffs.Safety & compliance: anti-hallucination pattern