01 Performance
Learners finish able to explain — in plain language — how modern AI systems learn, train simple models on real datasets, integrate AI APIs into working applications, and evaluate their own projects for accuracy, safety and fairness. The programme ends with a presented AI capstone project.
02 Objectives
- Distinguish rule-based systems from machine-learning systems
- Prepare data and train basic classification/regression models
- Evaluate models honestly using accuracy and error analysis
- Integrate AI APIs securely into applications
- Design prompts and guardrails for reliable AI behaviour
- Apply responsible-AI principles throughout a project lifecycle
03 Content
Foundations of AI
- What AI can and cannot do
- Rule-based systems vs learning systems
- Everyday AI around us
- Ethical & responsible use
Machine Learning Essentials
- Data, features & labels
- Classification vs regression
- Training your first model
- Evaluating model accuracy
Neural Networks & Deep Learning Concepts
- How neural networks learn
- Image recognition concepts
- Language models explained simply
Building with AI Tools & APIs
- Using AI APIs in Python
- Prompt engineering fundamentals
- Building an AI chatbot
- Generative AI for text & images
Capstone: Your Own AI Project
- Choosing a real problem
- Designing the solution
- Build, test & present
- Responsible deployment checklist
04 Knowledge
- AI vs machine learning vs deep learning — precise definitions
- Training/validation/test data concepts and why they matter
- How neural networks adjust weights during learning
- What large language models can and cannot do
- Bias, privacy and misuse risks in AI systems
05 Skills
- Cleaning and structuring small datasets for training
- Building and evaluating a model with guided tools/libraries
- Calling AI APIs with secret-key discipline
- Crafting and iterating effective prompts
- Testing an AI feature against edge cases and abuse
06 Key Competencies
- Critical evaluation of AI claims and outputs
- Ethical reasoning about automated decisions
- Experimental mindset: hypothesise, test, measure
- Clear explanation of technical systems to non-experts
07 Values
- Excellence: doing careful, complete work rather than rushing
- Integrity: honest effort, original work and truthful reporting
- Curiosity: asking questions and exploring beyond the lesson
- Responsibility: safe, ethical use of technology and information
- Growth mindset: treating mistakes as steps toward mastery
08 Learning Activities
'Is it AI?' clinics
Analysing everyday technologies to classify what kind of intelligence (if any) powers them.
Model-training lab
Hands-on session training an image or text classifier on a curated dataset.
Prompt engineering studio
Systematically improving prompts and measuring output quality.
Responsible-AI audit
Each capstone is peer-audited for fairness, privacy and failure modes before presentation.
09 Teaching & Learning Resources
- Dynamic Academy course materials and practice sets
- A laptop or desktop with required free software installed
- Internet access for research and tool accounts
- Access to the student portal for announcements, assignments and progress tracking
10 Evaluation Guide
11 Practical Projects
Project 1:First Classifier
Train and evaluate a model that recognises categories in images or text.
Project 2:AI-Powered Mini App
An application that uses an AI API for a genuinely useful task.
Project 3:Capstone: Responsible AI Solution
End-to-end project with documented testing, limitations and ethics notes.
12 Learning Outcomes
- Explain how AI, machine learning and deep learning relate
- Train simple models with real datasets
- Use AI APIs to build intelligent applications
- Build a working chatbot powered by a language model
- Apply responsible-AI principles when building solutions
13 Course Pathway
Recommended sequence: Python Programming first, then Artificial Intelligence, then AI Chatbot Development or Data Analysis. Advanced learners progress toward building production AI features in Backend Development.