Building GenAI Applications and Agents

This course unravels the intricacies of LangChain, a versatile framework for creating applications with language models. As AI and natural language processing become more integral to technological advancements, mastering LangChain is essential for modern developers. This course navigates through the basics of LangChain, background and context, practical uses in today's tech landscape, and prospects. Real-world examples illuminate how LangChain empowers developers to craft innovative, AI-driven applications. Main Outcome and Takeaways: Review and apply Langchain for Application development and essentials for Langchain Development. 1- Foundational Understanding: Acquire a solid grasp of LangChain's core concepts and architecture. (Knowledge) 2- Practical Application Development: Learn to build and deploy basic applications using LangChain. (Application) 3- Simple Problem-Solving: Utilize LangChain for solving straightforward programming tasks. (Comprehension) 4- Trends and Future Developments: Recognize potential future directions in LangChain and AI application development. (Analysis)

There are literally thousands of Large Language Models or LLMs available out there that can be used for a plethora of purposes. Hugging Face is the de-facto hub for language models, offering a huge collection where you can find and use almost any model you need. Choosing the right model can be an arduous task given models come in various shapes, sizes and configurations and each model is specialized at something different. So, when you approach Hugging Face in search of the right Model for your requirement, you have to know the art of this matchmaking. In this course, we will learn how to navigate through the Hugging Face Hub for Models, matching their configurations to your needs. We will understand key characteristics of Models (LLMs), such as Size, Computational Requirements, Specializations, Licensing and so on. We will look into various families of Models and their specializations, performance and variants. We will also learn how to use various models from Hugging Face and Evaluate them based on your requirements. This course is designed for professionals deeply involved in the field of AI and machine learning, including Data Scientists, Machine Learning Engineers, AI Engineers, LLM RAG Application Developers, Software Developers, and IT Engineers. It targets individuals who are actively building or plan to build applications leveraging Large Language Models (LLMs) and seek to enhance their ability to select and utilize the most appropriate models for their specific needs. Participants should have a strong foundation in Python programming and a basic understanding of Large Language Models (LLMs) and their programmatic use, as the course will build on these concepts with practical coding exercises and advanced topics like model selection, comparison, and evaluation. By the end of this course, learners will have achieved four key objectives. They will master navigating the Hugging Face ecosystem, gaining proficiency in finding and understanding various models. They will also learn to effectively use these models, comparing them based on multiple factors and practical considerations. Additionally, the course will guide participants in testing and evaluating different models, enabling them to score and assess the results based on specific parameters. Ultimately, learners will be equipped to select the most suitable model for a given task, ensuring optimal performance in their applications.

In this course, we start with the concepts and use of Large Language Models, exploring popular LLMs such as OpenAI GPT and Google Gemini. We will understand Language Embeddings and Vector Databases, and move on to learn LangChain LLM Framework to develop RAG applications combining the powers of LLMs and LLM Frameworks. The capabilities of LLMs are not to be kept confined within the tools like ChaGPT or Google Gemini or Anthropic Claude. You can leverage the powerful Natural Language Capabilities of LLMs applied on your organizational data to create amazing automations and applications that are called Retrieval Augmented Generation or RAG Applications. Some of the key components of the course are learning prompt Engineering for RAG Applications, working with Agents, Tools, Documents, Loaders, Splitters, Output Parsers and so on, which are essential ingredients of RAG Applications. Participants should have a basic understanding of Python programming and a foundational knowledge of Large Language Models (LLMs) to make the most of this course. By the end of this course, you'll be able to develop RAG applications using Large Language Models, LangChain, and Vector Databases. You will learn to write effective prompts, understand models and tokens, and apply vector databases to automate workflows. You'll also grasp key LangChain concepts to build simple to medium complexity RAG applications.

"Hey Siri, what's the weather today?" "Alexa, play my favorite playlist." AI is becoming as common as your morning coffee. Look at what's happening in the real world: Duolingo has transformed how millions learn languages by adding their ChatGPT-powered AI tutor that gives personalized explanations in seconds. Even Snapchat's chatbot "My AI" is having millions of conversations with users every day. The world of AI isn't some far-off sci-fi dream – it's happening right now, and developers like you are at the heart of it. Ready to build something awesome with AI? This course is unique because it doesn’t just stop at theory. You’ll gain hands-on experience in real-world API tasks, enabling you to build practical applications while learning to balance performance, cost, and API optimization effectively. By completing this course, you'll be able to seamlessly integrate ChatGPT API into your projects, quickly authenticate and configure parameters, analyze responses for improvements, and create practical AI-powered solutions that you can apply the very next day at work. By the end of this 2-hour course, you will be able to: - Explain the fundamental concepts of tokens, models, and rate limits in the context of the ChatGPT API. - Construct basic API calls to ChatGPT using proper authentication and parameters to generate appropriate AI responses for simple use cases. -Differentiate between various ChatGPT API use cases to select the most suitable model and parameters for specific application requirements. -Design effective prompts that optimize ChatGPT API responses for common use cases like content generation and text analysis. To be successful in this course, you should have: -Python installed on your computer. Familiarity with Python is recommended but not mandatory. -A free subscription to the OpenAI API. -A background in basic programming concepts and experience working with APIs. And to help you master these concepts, each lesson includes a practice quiz. You will also have a hands-on coding exercise to reinforce your learning and a final graded assessment that tests your ability to apply these skills in real-world scenarios. Want to get the most out of this course? Here's your blueprint for success: -Code along with the lessons—don't just watch. -Test your API calls with different parameters. -Keep track of your API usage and costs. -Document your error messages and solutions. -Complete all practice exercises before moving to the next lesson. Remember, this isn't just about passing a quiz—it's about building real-world skills you can use immediately. Take your time with the exercises, experiment with different approaches, and don't hesitate to revisit lessons when needed.

This course offers a fast-paced, hands-on introduction to the world of AI agents, perfect for aspiring AI architects and innovators. In just 75 minutes, you'll develop the skills to build AI agents that can understand, reason, and act in real-world scenarios. With a focus on efficiency and practical development, you'll dive straight into coding while gaining techniques that are scalable for future projects. This course is crafted for software developers, AI engineers, data scientists, and data and business analysts who are keen to implement AI in real-world scenarios. If you're interested in expanding your technical skills and gaining hands-on experience with AI agent development, this is the perfect starting point. Whether you're aiming to enhance existing applications, explore AI-powered solutions, or bring new ideas to life, this course equips you with essential skills for AI-driven innovation. This course is designed to be accessible to learners with a foundational understanding of Python programming and a general awareness of AI concepts; advanced AI expertise is not required. To participate fully, you’ll need a computer with a reliable internet connection, as the course involves hands-on coding exercises and interactive problem-solving. An openness to practical, step-by-step learning and real-world application is key, as this course emphasizes a mix of theory and immediate implementation. By the end of this course, learners will have the skills to apply core principles of AI agent architecture, enabling them to design and implement a basic agent system. You'll gain the capability to construct an efficient development environment for building and testing your AI agents, facilitating smooth workflows and testing processes. Additionally, you’ll develop a fully functional AI agent using frameworks like LangChain or AutoGen and learn to evaluate and optimize its performance through advanced feature integration, enhancing your agent’s effectiveness and adaptability in real-world applications.