Course – AI Fundamentals – Terminus System

Course Schedule

Self-Paced — Learn Anytime, Anywhere

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.

What you will learn

Who this course is 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

Learning Outcomes

Explain what Artificial Intelligence is and distinguish it from traditional software and programming.
Describe the relationship between AI, Machine Learning, Deep Learning, Generative AI, Foundation Models, LLMs, and AI Agents.
Explain how Machine Learning systems learn from data, including the roles of datasets, features, labels, training, validation, testing, and generalization.
Explain how models improve their predictions using concepts such as loss, gradient descent, and learning rate.
Recognize and explain underfitting and overfitting and understand why they affect real-world AI performance.
Understand common Machine-Learning approaches, including Decision Trees and K-Nearest Neighbors.
Interpret fundamental model-evaluation measures, including accuracy, precision, recall, F1 score, and confusion matrices.
Explain how Neural Networks and Deep Learning work at a conceptual level.
Describe how AI systems process images using Computer Vision and Convolutional Neural Networks.
Explain how machines represent and process human language, including embeddings, tokenization, and neural language models.
Explain the fundamental architecture and purpose of Transformers.
Describe how Large Language Models are developed and used, including pre-training, fine-tuning, human feedback, and inference.
Understand how Generative AI systems produce content such as text, images, code, and other forms of generated output.
Explain how Retrieval-Augmented Generation works and distinguish it conceptually from fine-tuning.
Explain what an AI Agent is and how agents use tools, workflows, memory, and iterative action cycles.
Recognize major limitations and risks of AI systems, including hallucination, bias, privacy, security, and misinformation.
Approach AI systems more critically and effectively, understanding both their capabilities and their limitations.
Build a coherent mental model of modern AI, from data and learning through models, generation, retrieval, tools, and human oversight.

Full program roadmap

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