Generative AI - The Ultimate Guide!

Explore how artificial intelligence evolves from machine learning to generative AI, contrasting supervised and unsupervised learning, with real estate and city planning examples illustrating content creation and pattern discovery.

Explore how GenAI evolved from chat-based answers to autonomous task completion and cross-application orchestration, becoming agentic, multimodal, and embedded in enterprise workflows by 2026.

Explore how diffusion models create images from text prompts by encoding text with clip, gradually adding Gaussian noise, and reversing the process to produce clear, styled visuals.

Train diffusion models to denoise and restore images by learning noise patterns and conditioning. Explore how sampling steps, seed variation, and input conditioning shape high-quality image generation.

Explore reasoning models that break complex problems into intermediate steps, generate verifiable reasoning paths, and improve accuracy through prompting techniques and training with supervised fine-tuning and RLHF.

Master prompt engineering for multimodal generative AI to process and create text, images, and more. See real-world examples, from spreadsheet explanations to graphing and screenshot-based user guides.

Explore chain-of-thought models and how to engineer prompts for structured, step-by-step reasoning. Learn when to use chain-of-thought prompting, create clear delimiters, and apply to coding, math, and science.

Explore enterprise AI tools across three types—AI assistants, AI built into existing software, and specialized tools—and learn to pick the right solution while protecting data.

Leverage specialized enterprise AI tools to provide domain knowledge, workflows, and integrations that general AI lacks. Employ these tools as autonomous collaborators across marketing, human resources, finance, and legal.

Compare open-source Gen-AI models with proprietary models, highlighting cost efficiency, transparency, customization, community support, and the risks of maintenance, security, and vendor lock-in.

discover how to run large language models locally using LM Studio, ensuring data privacy on your machine, choosing quantization levels, and interacting with downloaded models offline.

Learn to run image generation locally using Focus on Stable Diffusion, with a simple web UI, Windows installer, and optional Google Colab access.

Master prompt engineering for image generation by crafting clear, precise prompts and detailed contexts to control depth and mood in stable diffusion images.

Explore how retrieval augmented generation improves the accuracy of large language models by combining user questions with content store data, using vector similarity and prompt engineering to produce evidence-based answers.

Learn how to fine tune a pre-trained large language model for specific tasks, using supervised learning on domain data. Compare this with retrieval-augmented generation for practical AI applications.

Leverage generative ai to build apps that actively power user output. Use gen ai wrappers and apis to create a user-friendly ui and deploy a simple web app for non-programmers.

Build a simple web front-end that sends user questions to a Gen AI API like OpenAI and displays answers, while managing API keys and optional cloud or local models.

Explore artificial general intelligence as a form of AI with human-like reasoning, learning, and autonomous planning. The course maps levels from conversational AI to organizational AI and deep research.

Explore the possibilities and challenges of artificial general intelligence, from healthcare, coding, and autonomous vehicles to research insights. Examine perception limits, ethical considerations, and resource demands.