AI Agents & Automation

A guided project on Coursera where you learn to perform text summarization using the LangChain framework and Generative AI models. The project involves building a web application with Streamlit to make the summarization functionality interactive.

This Online Bootcamp is a compact and accelerated version of our 400-hour in-person master's program.It has four parts:- In Part 1, you will learn the keys to Artificial Intelligence and the new Generative AI, as well as its potential to revolutionize businesses, startups, and employment.- In Part 2, you will learn to build professional-level LLM Applications, the most potential applications of Generative AI. You will also learn how to build Advanced RAG LLM Apps, Multimodal LLM Apps, AI Agents, Multi-Agent LLM Apps, and how to manage LLM Ops.- In Part 3, you will learn how to build traditional and Gen AI apps without coding using Cursor AI and the new AI Coding Assistants. You will learn what are AI Coding Assistants like Cursor AI, Claude AI, v0, o1, Replit Agent, etc, and how to increase their performance by combining them with tools like the Replit platform, simplified backends like Firebase, Replicate AI, Stable Fusion, or Deepgram.- In Part 4, you will learn how to create SaaS applications without coding using Cursor AI. You’ll also see, through two high-level real-world examples, how Generative AI is transforming the SaaS (Software as a Service) model.By the end of this program, you will know how to do the following:AI AND BUSINESS Know the businesses that AI puts at risk of disappearing.Know the new opportunities created by AI for businesses.Design a plan to introduce AI into your company.Select an appropriate pilot project to introduce AI into your company.Form the first AI team in your company.Prepare your company's AI strategy.AI AND STARTUP Identify 100 opportunities to create AI startups.AI AND EMPLOYMENT Know the professions that AI puts at risk of disappearing.Know the new p

¡Descubre el fascinante mundo de ChatGPT y LL Ms en acción! Domina desde cero hasta convertirte en un experto en el curso "Guía Completa de ChatGPT y LL Ms en acción". Aprende a utilizar las poderosas herramientas de LL Ms y ChatGPT para impulsar tu conocimiento.  En un mundo cada vez más impulsado por la inteligencia artificial, los Modelos de Lenguaje Grande (LL Ms) desempeñan un papel fundamental en la sociedad. Estos modelos revolucionarios tienen el potencial de transformar la forma en que interactuamos con la tecnología, desde asistentes virtuales hasta chatbots y sistemas de recomendación personalizados. Dominar los LL Ms te permitirá impulsar la innovación, crear soluciones inteligentes y satisfacer las crecientes demandas de una sociedad cada vez más conectada. Con este curso, estarás preparado para marcar la diferencia en el mundo de la inteligencia artificial generativa y aprovechar al máximo el potencial que ofrecen los LL Ms en la sociedad, trabajo y vida diaria.¿Que aprenderás en el curso?Conviértete en un experto en modelos de lenguaje: Aprende desde los conceptos básicos hasta las técnicas avanzadas para aprovechar al máximo los LL Ms y transforma tus habilidades en el campo de la inteligencia artificial generativa.Aprende a utilizar herramientas clave: Descubre las herramientas esenciales, como la API de OpenAI, Hugging Face y LangChain, y domina su integración en tus proyectos de LL Ms y NLP.Mejora el rendimiento de tus modelos: Domina el Prompt Engineering y obtén resultados óptimos en tus interacciones con ChatGPT. Aprende a seleccionar el modelo adecuado y a aplicar técnicas de transfer learning para potenciar tus proyectos.Amplía las capacidades de ChatGPT y LangChain con LangChain y Agentes: Descubre cómo encadenar varios LL Ms y dale nuevas habilidades a tus modelos. Aprende a programar workflows y a i

Welcome to Building Generative AI Projects with LLM, LangChain, GANs course. This is a comprehensive project based course where you will learn how to develop advanced AI applications using Large Language Models, integrate workflow using LangChain, and generate images using Generative Adversarial Networks. This course is a perfect combination between Python and artificial intelligence, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in generative AI integration. In the introduction session, you will learn the basic fundamentals of large language models and generative adversarial networks, such as getting to know their use cases and understand how they work. Then, in the next section, you will find and download datasets from Kaggle, it is a platform that offers a diverse collection of datasets. Afterward, you will also explore Hugging Face, it is a place where you can access a wide range of ready to use pre-trained models for various AI applications. Once everything is ready, we will start building the AI projects. In the first section, we are going to build a legal document analyzer, where users can upload a PDF file, and AI will extract key information, summarize complex legal texts, and highlight important clauses for quick review. Next, we will develop an Excel data analyzer, enabling users to upload spreadsheets and leverage AI to identify trends, generate insights, and automate data analysis processes. Then after that, we will create an AI short story generator, where users can generate creative and engaging narratives based on simple prompts, making it a useful tool for writers and content creators. Following that, we will build an AI code generator, where users can input natural language descriptions, and AI will generate structured, functional code snippets, streamlining the coding process. In the next section, we will develop a Q&A customer support chatbot, capable of answering common inquiries b

Unlock the power of Generative AI and learn how to build real-world applications using cutting-edge tools like ChatGPT, LangChain, Hugging Face, and more — even if you’re not a developer.This course starts with a fast-track module for non-coders, introducing you to practical no-code AI tools like Zapier, Canva AI, and Notion AI. You’ll quickly understand how Generative AI works — no math, no jargon, just clear and practical insights.You’ll then dive deep into Large Language Models (LL Ms), learning how models like GPT and open-source alternatives function, and how to interact with them through effective prompt engineering. Understand the difference between OpenAI's AP Is, local models, and when to use each.The course progresses with hands-on projects using the OpenAI API and LangChain to build intelligent assistants, custom chatbots, and agent-based tools. You’ll explore how to integrate tools and functions, use Lang Graph for complex multi-step workflows, and build applications like weather and calculator agents.You'll also learn how to incorporate Hugging Face models, perform text classification, and explore LoRA fine-tuning basics — all with step-by-step guidance. The Retrieval-Augmented Generation (RAG) section will teach you how to connect AI with custom documents, PD Fs, and websites using embeddings and vector databases like Pinecone, ChromaDB, and FAISS.We’ll also cover critical topics like AI safety, bias, responsible prompt engineering, and deploying your apps using tools like Streamlit, Gradio, and Hugging Face Spaces. You’ll even learn how to add a simple frontend with HTML/CSS/JS to showcase your work live.By the end of the course, you’ll complete real-world capstone projects such as a Social Media Post Generator and a Podcast AI Summarizer, and learn how to build a portfolio on Git Hub that demonstrates your skills to potential clients or employers.Whether you're a developer, freelancer, entrepreneur, or aspiring AI bui

In this hands-on bootcamp, you will master Microsoft Co Pilot, GPT-5, and intelligent AI agents for data science. You’ll master the full data science workflow, including data wrangling and feature engineering, data cleaning and merging with Co Pilot. We will then cover data visualization and storytelling, turning raw data into dashboards and narratives that drive business decisions. You’ll also cover model development and validation, building and evaluating classifiers while tracking performance using metrics such as accuracy, precision, recall and ROC curves. Finally, you’ll cover anomaly detection, applying methods such as Z-Score and Isolation Forest to spot unusual patterns before they cost money.. What You’ll Learn:Clean and prepare real-world datasets using Co Pilot’s advanced prompt engineering.Build predictive models for forecasting, classification, and anomaly detection.Automate feature engineering and data wrangling tasks with custom AI agents.Visualize trends and correlations using Matplotlib, Seaborn, and Plotly inside Co Pilot.Detect anomalies using Z-Score and Isolation Forest techniques.Create executive-level insights and recommendations from raw data.Compare and evaluate multiple machine learning models with proper validation.Design custom GP Ts for advanced analysis, reporting, and business strategy.Bootcamp Modules:Co Pilot Overview & AI Agents Demo – From messy data cleanup to CEO-level storytelling.Data Wrangling & Feature Engineering in Co Pilot – Practical workflows for handling missing values, merging datasets, and creating features.Data Visualization in Co Pilot – Scatter plots, heatmaps, pairplots, and executive-ready dashboards.Model Development & Validation – Build, eva

The course "AI Agents for Everyone and Artificial Intelligence Bootcamp" is designed to demystify the world of intelligent systems, making it accessible to learners of all levels. Whether you're a curious beginner or an aspiring AI developer, this course provides a comprehensive foundation in the development, deployment, and application of AI agents across various domains. With a strong emphasis on hands-on learning, participants will explore state-of-the-art technologies such as machine learning, natural language processing (NLP), and advanced frameworks like AutoGPT, IBM Bee, Lang Graph, and CrewAI.Throughout the course, learners will gain a deep understanding of how AI agents function, from basic reflex agents to advanced collaborative systems. You'll learn about the core principles that govern intelligent agents, including decision-making, adaptability, and autonomy. By understanding these foundations, you will be equipped to create AI agents that can perceive their environment, make informed decisions, and perform complex tasks. The course also delves into the critical technologies that power AI agents, such as machine learning algorithms for predictive insights, NLP techniques for conversational AI, and robotics integration for automation.One of the course’s unique aspects is its focus on practical application. You will work on hands-on projects to develop and deploy AI agents in real-world scenarios. From creating collaborative systems with CrewAI to implementing stateful interactions using Lang Graph, you’ll get valuable experience with cutting-edge tools and frameworks. Additionally, the course explores the transformative potential of AI agents in industries such as healthcare, finance, business operations, entertainment, and IoT, providing actionable insights into their role in shaping the future.Ethics and societal impact are integral to this learning experience. The course examines the ethical considerations and regulatory challenges surrounding AI agents,

Autonomous agents, an intriguing advancement in the realm of artificial intelligence, are on the brink of reshaping our work dynamics and technological interactions. These intelligent entities transcend the role of mere tools; they function as digital collaborators capable of independently managing tasks to achieve specific objectives. Whether given vague directives or precise goals like creating a sales tracker tool, these agents autonomously navigate the task at hand, continually improving their efficiency until the desired outcome is achieved. This level of automation is revolutionary, akin to an indefatigable and highly efficient worker.Accessible to individuals with coding skills, operational autonomous agents are capable of handling diverse tasks, from app development to everyday chores, thereby saving valuable time and resources. Their potential lies in transforming industries, automating mundane tasks, and freeing individuals to focus on more creative pursuits.A notable project in the field of autonomous agents is Microsoft Research's Auto Gen. This innovative tool simplifies the development of conversational agents designed to solve problems through interactions with other agents, humans, and tools. The process involves defining conversable agents and interaction behaviors, analogous to scripting a play where the user determines how agents engage in and progress through the conversation.Auto Gen's agents possess the ability to interact and collaborate, essentially functioning as a team. Leveraging Language Models (LL Ms), human input, and tools, these agents understand language, generate ideas, and make logical decisions. The central role of LL Ms supports various agent configurations, including those fine-tuned on private data. Developers can adjust human participation levels, and tools act as specialized utilities to overcome LLM limitations.Auto Gen distinguishes itself with features like unified conversation interfaces, facilitatin

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 & 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 & 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&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.

Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting AP Is like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to:Understand the fundamentals of AI and LangChain Set up your development environment from scratch Build and test working AI agents using Python and OpenAI Extend your agents with real-world tools and AP Is Join today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.

This comprehensive course is your one-stop guide to learn Python Basics, Popular Data Manipulation Libraries, Deep Learning Fundamentals, Popular Generative AI Models, Large Language Models and Agentic AI frameworks, all in one place. Whether you're a beginner exploring the world of AI or a developer looking to level up, this course takes you from the ground up and beyond.We begin with Python fundamentals and dive into essential data libraries like Num Py, Pandas, and Matplotlib for effective data handling and visualization. Then, we advance into Deep Learning, building and training neural networks Mode to understand the core mechanics behind AI.Generative AI is a subset of Deep Learning. Without a solid understanding of Deep Learning fundamentals, learning Generative AI becomes difficult and often confusing. That’s why I’ve combined the most essential parts from one of my previous Deep Learning courses into this course. This ensures that you build a strong foundation before diving into advanced Generative AI topics.Once the Deep Learning Fundamentals is complete, You’ll then explore the rapidly evolving field of Generative AI:From training your own GANs and VAEs, to working with Large Language Models (LL Ms), Retrieval-Augmented Generation (RAG), and Diffusion Models, this course offers hands-on projects and intuitive explanations.Finally, we introduce you to the next frontier: Agentic AI. Learn about intelligent agent architectures such as MCP, ACP, and A2A, and use cutting-edge frameworks like LangChain to build autonomous, goal-driven AI agents.What You’ll Learn Python programming basics and data manipulation using Num Py and Pandas Data visualization using Matplotlib Fundamentals of Deep Learning and neural network training Building Generative AI models: GANs, VAEs, LL Ms, and Diffusion Models Implementing Retrieval-Augmented Genera

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 API for your LangChain Application just

Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting AP Is like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to: Understand the fundamentals of AI and LangChain Set up your development environment from scratch Build and test working AI agents using Python and OpenAI Extend your agents with real-world tools and AP Is Join today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.

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   AI 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

Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting AP Is like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to: Understand the fundamentals of AI and LangChain Set up your development environment from scratch Build and test working AI agents using Python and OpenAI Extend your agents with real-world tools and AP Is Join today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.

In this course you'll learn about this new way of using LLM Agents: deploying multiple agents to work together as teams to accomplish more complex tasks for you!Everything is taught step by step and the course is fully practical with multiple examples and one complete AI Agents-based App that we build together.One of the things we use to accomplish this is ChatGPT's API so we can use ChatGPT through Python.We also use Auto Gen to enable our Agents to work together and communicate with one another (to accomplish tasks with no human intervention).We also provide a few optional sections. One of these sections teaches to have a front-end, using Streamlit, to more easily interact with your AI Agents.Another optional section is for those who want to run AI Agents at scale! Here we show you how to deploy your LLM Agents on Google Cloud, so anyone can use your product.Lastly, one more optional section is available showing how to set up a payment system/subscription model using Stripe for those who want to monetize their AI Agents-based App!Everything is explained simply and in a step-by-step approach. All code shown in the course is also provided.Please not that the OpenAI API is not free, you will need to fund your OpenAI developer account with about $5-10 to follow through with the class and build your own app. We clearly show and explain how to do this and minimize your OpenAI costs during this class.