[Free] Artificial Intelligence For Beginners: Ai Terms Explained
Understand Generative AI, Machine Learning, LLMs, AI Agents and today’s essential AI concepts
What you’ll learn
- The difference between AI, Machine Learning, and Deep Learning
- What neural networks, models, parameters, and Transformers mean
- How LLMs, tokens, context windows, training, and inference work
- What Computer Vision, NLP, and Generative AI are used for
- How embeddings, vector databases, and RAG work together
- What prompt engineering and AI hallucinations mean
- How AI agents, Agentic AI, and workflow automation differ
- Why Responsible AI, privacy, fairness, and human oversight matter
Requirements
- No technical background required
Description
Artificial Intelligence is everywhere – but so is the terminology. Machine Learning, Deep Learning, Large Language Models (LLMs), tokens, embeddings, Retrieval-Augmented Generation (RAG), AI agents, multimodal AI, fine-tuning, hallucinations, responsible AI, and many other terms appear constantly in business and technology conversations.
This practical course is designed to make those concepts clear and easy to understand, without requiring a coding, mathematics, or data science background. It explains the essential language of modern AI in simple terms, using practical examples and business-relevant context.
You will learn how AI and Machine Learning are related, how modern AI systems work, what makes Generative AI different, how Large Language Models process information, how AI models are trained, and how technologies such as embeddings, vector databases, and RAG help AI retrieve and use information more effectively.
The course also covers important limitations and risks, including hallucinations, bias, explainability, responsible AI, privacy, and human oversight. You will also explore emerging concepts such as Agentic AI, Autonomous AI, Edge AI, and AI-powered workflow automation.
By the end of the course, you will have a practical understanding of the most commonly used AI terms and a clear mental model of how today’s AI technologies fit together. The goal is not to turn you into an AI engineer, but to help you participate in AI discussions with greater confidence, ask better questions, and better evaluate AI opportunities in both business and everyday life.
Author(s): KRAGON AI
