Course – AI Fundamentals

Description

Artificial Intelligence is rapidly becoming part of how organizations work, make decisions, create content, automate processes, and interact with technology. Yet understanding AI requires more than knowing how to use an AI chatbot or write a prompt.

AI Fundamentals provides the conceptual foundation needed to understand what AI is, how machines learn from data, how neural networks work, how modern AI processes images and language, and how today’s Generative AI, Large Language Models, and AI Agents are built.

The course begins with the fundamentals of Artificial Intelligence and establishes a clear relationship between AI, Machine Learning, Deep Learning, Generative AI, Foundation Models, LLMs, and AI Agents.

Learners then explore how Machine Learning systems learn from data, including datasets, features, labels, training, validation, testing, loss, generalization, gradient descent, learning rate, model capacity, underfitting, and overfitting.

The course progresses into Neural Networks and Deep Learning before examining two major areas of AI application: Computer Vision and Natural Language Processing. Learners are introduced to concepts such as convolutional neural networks, embeddings, tokenization, and recurrent neural networks.

The course then moves into the technologies behind modern Generative AI. Learners develop a conceptual understanding of Transformers, attention, tokens, embeddings, foundation models, Large Language Models, pre-training, supervised fine-tuning, human feedback, and prompt engineering.

Finally, the course explores how AI systems can work with external knowledge and perform actions through Retrieval-Augmented Generation (RAG), fine-tuning, AI Agents, tools, workflows, and memory.

Throughout the course, technical concepts are explained from a beginner’s perspective, with emphasis on understanding what each technology does, why it exists, how it relates to other AI technologies, and where it is used.

The course also addresses important limitations and responsible-use considerations, including hallucination, bias, sycophancy, deepfakes, privacy, security, verification, and human oversight.

By the end of the course, learners will have a coherent mental model of modern AI rather than simply a collection of disconnected AI terms.

Who Should Attend?

This course is designed for beginners who want to understand Artificial Intelligence from the ground up.

It is suitable for:

Professionals who want a solid foundation in AI

Business managers and decision-makers

Entrepreneurs and business owners

IT and technology professionals entering AI

Cybersecurity and information-security professionals

Students and recent graduates

Educators and trainers

Professionals preparing for AI-related roles

Anyone who wants to understand how modern AI systems work

Professionals who use Generative AI but want to understand the technology behind it

No Prior AI Experience Required

You do not need:

• Previous AI knowledge
• Machine-learning experience
• Advanced mathematics
• Advanced statistics
• Programming experience

Basic familiarity with computers and digital technologies is helpful but not required.

What You Will Learn

By completing this course, you will develop an understanding of:

AI Foundations

Machine Learning

Data and Model Learning

Machine-Learning Models

Neural Networks and Deep Learning

Computer Vision

Natural Language Processing

Generative AI and LLMs

Modern AI Systems

AI Limitations and Responsible Use