The Pearson course outline begins with a program-level introduction before progressing through the full lesson sequence.

• Introduction

Build a foundation in core AI concepts, practical uses, benefits, and challenges.

• Introduction
• Lesson 1.1: AI Concepts
• Lesson 1.2: Uses for AI
• Lesson 1.3: Benefits of AI
• Lesson 1.4: Challenges of AI
• Lesson 1 Summary

Learn how to frame machine learning problems and choose appropriate AI or ML tools.

• Introduction
• 2.1 Machine Learning Workflow
• 2.2 Formulate the Machine Learning Problem
• 2.3 Select AI/ML Tools
• Lesson 2 Summary

Cover the data collection and preparation workflow needed for effective AI analysis and modelling.

• Introduction
• 3.1 Collect and Assess Data
• 3.2 Extract Data
• 3.3 Transform Data
• 3.4 Load Data
• Lesson 3 Summary

Understand how to examine, visualize, and preprocess data before using it in AI and ML workflows.

• Introduction
• 4.1 Examine Data
• 4.2 Analyze Data Distribution
• 4.3 Visualize Data
• 4.4 Preprocess Data for AI and ML
• Lesson 4 Summary

Move from data understanding into practical model design and hypothesis testing.

• Introduction
• 5.1 Identify ML Algorithms
• 5.2 Test a Hypothesis
• Lesson 5 Summary

Train, tune, and evaluate classification models for category-based predictions.

• Introduction
• 6.1 Select, Train, and Tune Classification Models
• 6.2 Evaluate Classification Models
• Lesson 6 Summary

Build regression workflows focused on numeric prediction, regularisation, and model evaluation.

• Introduction
• 7.1 Train Regression Models
• 7.2 Regularize Regression Models
• 7.3 Evaluate Regression Models
• Lesson 7 Summary

Learn how clustering models are trained, tuned, and evaluated for unsupervised learning scenarios.

• Introduction
• 8.1 Train and Tune Cluster Models
• 8.2 Evaluate Cluster Models
• Lesson 8 Summary

Explore project launch considerations including security, privacy, ethics, and communication of results.

• Introduction
• 9.1 Security and Privacy in AI/ML Projects
• 9.2 Considerations for Ethical Use of AI/ML
• 9.3 Communicate Results
• Lesson 9 Summary

Focus on deployment, testing, and production monitoring for real-world AI and ML solutions.

• Introduction
• 10.1 Communicate Model Capabilities and Limitations
• 10.2 Deploy and Test Models in Apps
• 10.3 Support and Monitor AI/ML Solutions
• Lesson 10 Summary

Practice tests help learners revise the full course pathway and prepare for certification with greater confidence.

• IT Specialist: Artificial Intelligence Official Practice Tests

Certification scheduling and support information can be arranged with MCCA as part of your enrolment pathway.

• Scheduling and Information (1 Question)