Generative AI for Beginners

Explore the basics of generative AI and its relation to artificial intelligence, machine learning, and deep learning, plus how it generates new content across text, image, audio, and code.

Explore the fundamentals of artificial intelligence, machine learning, and deep learning, and learn how neural networks drive advanced generative tools like ChatGPT.

Explore how generative ai, a subset of deep learning, uses large training data to generate new text, images, and content, unlike conventional ai that only predicts or classifies.

Explore ChatGPT and other LLM-powered chatbots to see natural language understanding, conversational context, and capabilities in text, code, and image generation for emails, documents, and more.

Explore essential generative AI terms like LLMs, prompts, embeddings, chatbots, and agentic AI, and learn how these concepts work across models in a simple, interactive module.

Explore large language models and generative AI, their text generation capabilities, trained on vast data and parameters, and use cases like content generation, chatbots, translation, summarization, and Q&A.

Master prompt engineering as a core generative AI skill by crafting clear, context-rich prompts and refining outputs through iterative testing and fine tuning.

Embeddings convert text into numerical representations that capture meaning, context, and relations, enabling transformers and language models to understand text and predict the next word.

Fine tuning adapts a pre-trained foundation model to a specific data set for domain-focused results. Learn self-supervised, supervised, and reinforcement learning approaches, and remember data quality and iteration matter.

Explore how large language models generate text by using embeddings and transformers, and learn how prompt engineering and fine tuning shape better results across LMS and domain data.

Learn how retrieval augmented generation (rag) combines retrieval from your own knowledge sources, embedding-based vector stores, and targeted LLM responses to deliver authoritative, private, and hallucination-reduced answers with references.

Discover agentic AI, where AI agents plan, decide, and execute tasks autonomously toward a goal. See how tools, memory, knowledge and an LLM-driven supervisor guide reasoning with minimal human input.

Explore how generative AI disrupts industries and unlocks the potential in key sectors by enhancing existing use cases like chatbot, predictive marketing, and sentiment analysis.

Explore how generative AI streamlines build, testing, and requirement analysis in software development, from code generation to documentation.

Explore how generative ai in retail drives product description creation, personalized recommendations, and targeted marketing, enhances supply chain decisions, and enables multilingual customer support and sentiment analysis.

Apply generative AI to speed content creation, personalize ads, and enhance SEO and market research in marketing. Use AI as a collaborative companion, with humans guiding strategy and creativity.

Explore a hands-on introduction to building a ChatGPT-like chatbot that queries our own data, asks questions, and gets answers, built from scratch with Python API integrations.

Set up a rack chatbot using OpenAI GPT-LLM, LangChain, and Streamlet. Clarify objectives and architecture; ingest documents, chunk content, create embeddings, and store them in a vector store.

Learn to build a pdf chatbot using python and streamlit, set up a virtual environment, manage requirements, upload and read pdfs, and chunk text for embeddings.

Generate embeddings from text chunks using OpenAI embeddings, store them in a vector store with TextEmbedding3Small, and integrate PDF ingestion and chunking in a LangChain workflow.

Create a user initiated chatbot by converting questions into embeddings, performing similarity search on a vector store, and generating answers with an OpenAI LLM guided by prompts.

Explore ethical considerations and future trends in generative AI, building on the foundations of AI and ML, including prompt engineering, embeddings, and fine-tuning, with a hands-on chat bot reflection.

Explore responsible AI by examining bias, privacy, and transparency challenges in generative AI. Learn ethical principles, data quality, consent, and human-in-the-loop practices to build safe, trustworthy systems.

Generative AI is rapidly evolving, shifting from ideas to practical, domain-specific, smaller models and multimodal capabilities, and will not replace humans but drive a skill shift for knowledge workers.