Course Details

ICA Online Diploma in Artificial Intelligence (Level 6)

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Diploma Level 6

ICA Online Diploma in Artificial Intelligence (Level 6)

Duration:

1 Year Full-Time
(2 Years Part-Time)

Total Credits:

120

Programme Level:

06

Programme Overview

The ICA Online Diploma in Artificial Intelligence (Level 6) is an advanced, applied programme covering AI strategy, production-ready AI systems, data engineering, applied mathematics and statistics, machine learning, deep learning, generative AI engineering, governance, ethics, and compliance.

Students will design AI adoption approaches, build production-ready pipelines, engineer data and features, develop and deploy machine learning and generative AI solutions, conduct organisational AI compliance audits, and complete an industry-focused capstone project.

Target Students
  • Level 5 qualification holders progressing to specialised study
  • IT professionals seeking to specialise in AI
  • Business professionals leading AI adoption in their organisation
  • Career changers with existing technical or business experience
  • International students with a Level 5 qualification or equivalent experience
  • Software developers advancing into specialised AI engineering roles
Course Structure

The programme consists of eight courses worth 15 credits each, delivered across two semesters for a total of 120 credits.

Course Content
Semester 1
Topics
  • History of AI
  • Types of AI
  • Narrow AI vs AGI
  • AI applications
  • AI in business
  • AI trends
  • AI careers
  • AI strategy and governance
  • Competitive advantage through AI
  • Emerging AI research trends
Learning Outcomes
  • Critically evaluate AI concepts and their strategic business value
  • Analyse emerging AI technologies and trends for organisational impact
  • Design an AI adoption approach appropriate to a specific business context
Topics
  • Python basics
  • Variables
  • Loops
  • Functions
  • Object-oriented programming
  • File handling
  • APIs
  • Virtual environments
  • Design patterns
  • Asynchronous programming
  • Automated testing and debugging
  • Software architecture for AI systems
Practical
  • Build production-ready AI pipelines
  • Integrate multiple APIs into a single workflow
  • Implement automated tests for data and model code
Topics
  • Databases
  • SQL
  • CSV
  • Excel
  • Data cleaning
  • Pandas
  • NumPy
  • Data visualisation
  • Data warehousing
  • Big data concepts
  • Feature engineering
  • ETL pipelines
  • Data governance and quality
Practical
  • Design data pipelines for AI projects
  • Engineer features for machine learning models
  • Ensure data governance and quality compliance
Topics
  • Statistics
  • Probability
  • Linear algebra
  • Graphs
  • Matrix basics
  • Correlation
  • Multivariate calculus basics
  • Optimisation techniques
  • Eigenvalues and eigenvectors
  • Bayesian statistics
  • Regression

Note: This course builds sufficient theoretical depth to support model design, evaluation, and interpretation.

Semester 2
Topics
  • Supervised learning
  • Unsupervised learning
  • Regression
  • Classification
  • Clustering
  • Model evaluation
  • Neural networks
  • Deep learning basics
  • Ensemble methods
  • Hyperparameter tuning
  • Cross-validation
Tools
  • Scikit-Learn
  • TensorFlow / Keras
Projects
  • Forecast sales using ensemble models
  • Deploy a customer classification model as an API
Topics
  • Large language models (LLMs)
  • ChatGPT
  • Claude
  • Gemini
  • Copilot
  • Prompt engineering
  • AI agents
  • Retrieval-augmented generation (RAG)
  • AI productivity
  • Fine-tuning LLMs
  • Vector databases and embeddings
  • Agentic AI workflows
  • Multi-modal AI
Practical
  • Design and deploy AI agents for business workflows
  • Business automation
  • Build RAG-based knowledge assistants
Topics
  • AI ethics
  • Privacy
  • Copyright
  • AI bias
  • Hallucinations
  • AI governance
  • New Zealand Privacy Act
  • AI regulations
  • International AI regulation (EU AI Act and GDPR)
  • AI audit and assurance
  • Algorithmic accountability
Project
  • AI risk and compliance audit for an organisation

Students build a real AI solution.

Examples
  • Enterprise AI chatbot with RAG
  • AI-powered customer support automation platform
  • AI-powered marketing analytics assistant
  • AI document search and knowledge management system
  • Multi-modal AI voice assistant
  • AI-driven CRM and sales forecasting assistant
Assessment
  • Presentation
  • Portfolio
  • Technical report
  • Industry demonstration
Qualification Awarded
ICA Online Diploma in Artificial Intelligence (Level 6)

Awarded by: International College of Auckland (ICA)

Study Information

Credits: 120

Duration: 1 year full-time or 2 years part-time

NZQF Level: Level 6

Study Focus: Advanced theory and practical application