Generative AI & AI Agents Made Easy for Everyone

Discover how generative AI, built on deep learning and massive training data, moves beyond classification to generate original images, audio, code, and ideas.

Demonstrates how generative AI learns by comparing it to mastering music through thousands of songs, highlighting rhythm, melody, style, transitions, and patterns that enable new human like content.

Generative AI requires massive data to learn patterns, relationships, and grammar, enabling it to generate high-quality text, code, and images.

Explore self-supervised learning where AI learns from raw data by predicting missing pieces, with data providing the questions and answers and no human labels needed.

A large language model, or LLM, reads and writes text like a human. Trained on massive data, it can answer questions, translate between languages, and summarize long documents.

Large language models rely on training on a huge amount of text and billions of parameters, enabling smarter predictions, better understanding, and handling complex conversations with creative, accurate writing.

Explore the AI brain inside an LLM by examining its parameters, training, and fine tuning, and learn how these blocks shape language understanding and specialized performance.

Learn how to craft clear, specific prompts to elicit accurate, high-quality responses from generative AI models. This course explains prompt engineering, with real-world examples and best practices for beginners.

Discover how to design prompts that guide AI responses by specifying audience, format, length, and style, with real-world examples of tailored explanations.

Define clear, specific prompts and set context to guide AI responses. Specify format and audience, test variations, and refine prompts to avoid overload and generate accurate replies for beginners.

Explore how embeddings translate words into numbers that capture meaning, enabling AI to relate words like cat and dog through similar vectors and distinguish others like car.

Explore how chat GPT processes questions through tokenization, embedding, and meaning comparison with training data to predict the best answer, demystifying it as math patterns rather than magic.

Embeddings convert words into meaningful numerical vectors that encode meaning, context, relationships, and emotions, so similar terms cluster while operations like king minus man plus woman yield queen.

Discover how AI learns embeddings by reading vast text and spotting word patterns and co-occurrence. See how it converts words into numeric vectors through unsupervised learning.

Tokenization breaks human language into tokens, mapping each to a unique token ID and then to embeddings, so AI processes text as numbers rather than words.

Drive AI cost, limits, and performance by shaping input and output prompts. Longer prompts and responses raise costs, and token limits can drop old messages.

Master retrieval augmented generation (rag) to enable open-book AI that answers from hospital documents, pdfs, and internal data, reducing hallucinations and delivering trusted, up-to-date information.

Explore how retrieval augmented generation improves hospital AI accuracy and compliance by retrieving real policy documents to answer staff questions, ensuring secure communications and reducing errors.

Learn how fine-tuning turns general AI into specialized, organization-specific experts by training on your data, workflows, and tone to improve accuracy, voice, and cost efficiency.

Learn how fine-tuning uses a three-step process—prepared training dataset, training, and evaluation—to tailor AI to your style and domain, then test on unseen questions.

Explore a real-world hospital case study of fine-tuning a general AI into a hospital-ready medical assistant by training on internal Q&A and hospital document formats.

Understand AI hallucination, why it is dangerous in healthcare, law, and finance, and how to reduce it with retrieval augmented generation, real-document checks, fine-tuning, and human review.

A case study shows how AI hallucinations mislead nurses about sharing patient lab reports on WhatsApp, and how retrieval-augmented generation grounds answers in hospital policies to protect privacy.

Understand what an AI agent is and how it works by seeing how it observes, reasons, acts, and learns automatically to achieve a goal, like a proactive digital teammate.

Explore the life cycle of an AI agent, from goal and perception through four phases—reasoning, action execution, and reflection and learning—to production across frameworks.

Learn how AI agents form clear goals from user requests, perceive context from calendars and history, and apply governance to control data access and retries.

Learn how ai agents perform reasoning and planning by breaking goals into steps, evaluating options, and applying governance, explainability, fairness, api limits, and decision rules to select the best flight.

Phase 3 moves the AI agent from planning to action by interfacing with real systems to book flights and process payments, under strict governance with safety breaks and human approvals.

Reflecting on phase 4 of the AI agent, the system emphasizes learning, evaluating actions, and user happiness, while preventing sensitive data from long-term memory via encryption and audit logs.

Outline the AI agent architecture with four layers—profile, memory, planning, and action—and an execution flow, illustrating a travel assistant using role, permissions, and remembered preferences to complete bookings.

Define the AI agent’s profile as its foundation, detailing its role, domain, rules, style, and safety boundaries for a travel booking assistant operating on travel APIs.

explore how ai agents use memory to personalize and stay safe, detailing short-term, long-term, rag, and episodic memory, what to store, and how data minimization, encryption, and audits govern memory.

Examine the action layer of an AI agent and its dangers as it executes irreversible real-world tasks, requiring strict governance, approvals, and action logging.

Join a hands-on lab to build your first AI agent from scratch with CrewAI, configuring a node writer task that saves beginner-friendly nodes to nodes.md in the news agent project.

Take a hands-on lab to install Krew AI, configure Python and uv, and build your first AI agent using a News Agent project with an OpenAI key and task definitions.

Explore four types of AI agents: assistive, human in the loop, human on the loop, and fully autonomous, and how rising autonomy increases risk and governance needs.

Assistive AI agents function as helpers that wait for human input, offering suggestions like spell check or email phrases, with low autonomy and basic monitoring and content filters.

Understand how the human in the loop AI agent analyzes data, drafts decisions, and makes recommendations, with humans reviewing and approving final actions like loans, refunds, and emails.

Human on the loop AI agents operate with high autonomy under continuous human monitoring. They cannot function without oversight or make high-risk decisions alone.

A fully autonomous agent acts, learns, and decides without human involvement, posing high risk and requiring strict governance, monitoring, and incident response playbooks to prevent unsafe or biased outcomes.