Practical AI Skills & Hands-On Implementation (Module 2)

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About Course

Module 2: Practical AI Skills & Hands-On Implementation introduces learners to the practical, real-world application of Artificial Intelligence. This module forms the practical component of the Occupational Certificate: AI Software Developer and focuses on applying theory in workplace and simulated environments.

Learners work through structured practical activities that build confidence and competence in real AI development tasks. The module includes supervised tasks, demonstrations, tool usage, workplace simulation, coding exercises, and logbook-based assessments. Each Practical Module (PM) guides the learner to apply AI concepts using Python, SQL, machine learning techniques, model evaluation methods, and ethical teamwork practices.

By completing this module, learners will gain the ability to:

  • Set up and work with AI development tools
  • Handle real datasets for AI projects
  • Train and evaluate machine learning models
  • Write and execute Python scripts for AI tasks
  • Use SQL to manage and manipulate database tables
  • Build neural networks in Python and TensorFlow
  • Understand ethical and multidisciplinary team practices
  • Record practical experience in logbooks
  • Demonstrate competency through workplace tasks and mentor evaluations

This module prepares the learner for real AI roles by integrating technical skills, workplace expectations, and applied problem-solving.

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Course Content

Topic 1: Practical AI Skill Foundations (PM-01)
This topic introduces the structure, expectations, and responsibilities involved in completing practical AI modules, preparing learners for real workplace application.

Topic 2: AI Data Handling & Preparation (PM-02)
This topic covers the essential data skills required for AI development, including collecting, cleaning, preparing, and describing datasets. Learners will practice working with real data to support machine learning and AI modelling tasks.

Topic 3: Machine Learning Fundamentals (PM-03)
This topic introduces the practical steps involved in preparing datasets for machine learning, training simple models, evaluating model performance, and understanding how AI systems learn from data.

Topic 4: AI Development Tools & Environment Setup (PM-04)
This topic introduces learners to AI development environments, including installing essential software, configuring tools, setting up Python, and preparing the system for building machine learning and AI models.

Topic 5: Model Training & Testing (PM-05)
This topic teaches learners how to train machine learning models, adjust parameters, test performance, and understand the behaviour of AI models during training and evaluation.

Topic 6: AI Model Performance Evaluation (PM-06)
This topic teaches learners how to evaluate the performance of machine learning models using metrics such as accuracy, precision, recall, error rates, and confusion matrices. Learners will learn to interpret results and identify areas for model improvement.

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