AI Foundations Technologies, Applications & Practice Grade 10-12

Explore the technologies behind modern Artificial Intelligence! Our “AI Foundations: Technologies, Applications & Practice” course introduces learners to the key technologies that power modern AI systems. Discover how machine learning, deep learning, generative AI, large language models (LLMs), natural language processing (NLP), data science, cloud technologies, cybersecurity, and MLOps work together to build, deploy, and maintain AI solutions.

Through practical case studies and assessments, learners connect technical concepts with real-world scenarios, explore how AI systems are developed and used, and build a structured understanding of the modern AI ecosystem.

Why choose our course:

• Explore the AI landscape: Understand what artificial intelligence is, discover major AI model families, and learn how different technologies address different tasks.
• Understand machine learning and deep learning: Explore core concepts, examine how neural networks learn from data, and investigate the basic process of training an AI model.
• Discover generative AI, LLMs, and NLP: Learn how generative AI creates content, how language models process text, and how prompting and Retrieval-Augmented Generation (RAG) support AI applications.
• Connect data science with cloud technologies: Explore data pipelines, understand the role of data in AI projects, and examine how cloud platforms support data processing and AI workloads.
• Explore cybersecurity and MLOps: Understand key security considerations and discover how AI and machine learning systems can be deployed, monitored, and maintained responsibly.
• Apply knowledge through practical case studies: Work through realistic scenarios, analyse technical choices, complete assessments, and develop the skills needed to engage with modern AI tools and technologies more effectively.

Course Syllabus

Module 1: Opening & AI Landscape

• Reading: Opening & AI Landscape
• Video: Opening & AI Landscape — What AI Is and Major Model Families
• Case Study 1: Mapping AI Landscapes to a Use Case
• Assessment

Module 2: ML & DL Foundations

• Reading: ML & DL Foundations
• Video: ML & DL Foundations — Core Concepts
• Case Study 2: Training a Toy Neural Network
• Assessment

Module 3: GenAI, LLMs & NLP

• Reading: GenAI, LLMs & NLP
• Video: GenAI, LLMs & NLP — Prompting & RAG
• Case Study 3: Prompting a Simple Task
• Assessment

Module 4: Data Science & Cloud

• Reading: Data Science & Cloud
• Video: Data Science & Cloud — Pipelines & Cloud Choices
• Case Study 4: Cloud-Enhanced Data Pipeline
• Assessment

Module 5: Cybersecurity & MLOps

• Reading: Cybersecurity & MLOps
• Video: Cybersecurity & MLOps — Secure Deployments
• Case Study 5: Securing an ML Deployment
• Assessment

Final Exam

• Case Studies
• Assessment

General Information

The course combines readings, instructional videos, practical case studies, and assessments. Each module introduces key AI technologies and concepts and provides opportunities to apply them to practical scenarios.

The learning sequence moves from an overview of the AI landscape and the foundations of machine learning and deep learning to generative AI, language technologies, data science, cloud infrastructure, cybersecurity, and MLOps. This structure helps learners understand how these technologies connect and contribute to the development and deployment of modern AI systems.

Practical case studies encourage learners to connect theory with real-world applications, analyse technical scenarios, consider implementation choices, and develop a clearer understanding of how AI technologies are used in practice.

The course concludes with a Final Exam that combines case-study tasks and assessment questions to review and consolidate the key concepts covered throughout the course.

Estimated duration: Approximately 12 hours.

Recommended pace: 3–5 hours per week.

Learners can complete the course at their own pace.

Expected Learning Outcomes

By the end of this course, learners will be able to:

• Describe the modern AI landscape and identify major AI model families and their typical applications.
• Explain the basic principles of machine learning and deep learning and describe how neural networks learn from data.
• Describe the role of generative AI, large language models (LLMs), and natural language processing (NLP) in modern AI applications.
• Explain the basic principles of prompting and describe how Retrieval-Augmented Generation (RAG) can connect language models with external information sources.
• Identify the role of data science and data pipelines in preparing and processing data for AI systems.
• Explain how cloud technologies support data processing, AI workloads, and the deployment of AI applications.
• Recognize key cybersecurity considerations when developing, using, and deploying AI and machine learning systems.
• Describe the purpose of MLOps and explain how it supports the deployment, monitoring, and maintenance of machine learning systems.
• Analyse practical AI scenarios, compare possible technical approaches, and identify relevant technologies for a given use case.
• Apply course concepts to case studies and demonstrate an integrated understanding of how modern AI technologies work together.

By completing the course, learners develop a broad foundation in modern AI technologies and a clearer understanding of how AI systems are built, used, deployed, and maintained. They gain practical experience analysing AI-related scenarios and build the technical awareness needed for further learning in Artificial Intelligence, Machine Learning, Data Science, cloud technologies, cybersecurity, and related fields.

Recommended Skills Prior to Taking This Course

Learners should have:

• Basic computer and digital skills.
• Experience using web browsers and online learning platforms.
• The ability to read and follow technical instructions.
• Basic familiarity with data, charts, and tables.
• A willingness to explore technical AI concepts and learn how modern technologies work.

Basic programming or Python knowledge is helpful but not required.

Recommended Technology

A computer or laptop with a reliable internet connection and a current version of Chrome, Edge, Firefox, or Safari is recommended to access the course materials, videos, case studies, and assessments.