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.
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Lesson 1: Introduction to Practical AI Skills
Quiz 1: Practical AI Skill Foundations
00:30:00
Assignment 1: Understanding Practical AI Responsibilities (PM-01)
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.
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Lesson 1: AI Data Handling & Preparation
Quiz 2: AI Data Handling & Preparation
00:30:00
Assignment 2: Data Cleaning & Preparation Task (PM-02)
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.
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Lesson 1: Machine Learning Fundamentals
Quiz 3: Machine Learning Fundamentals
00:30:00
Assignment 3: Machine Learning Model Development (PM-03)
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.
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Lesson 1: Setting Up AI Development Tools & Environments
Quiz 4: AI Development Tools & Environment Setup
00:30:00
Assignment 4: AI Development Environment Setup (PM-04)
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.
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Lesson 1: Model Training & Testing
Quiz 5: Model Training & Testing
00:30:00
Assignment 5: Model Training & Performance Evaluation (PM-05)
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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Lesson 1: Understanding AI Model Performance Evaluation
Quiz 6: Performance Evaluation in Machine Learning
00:30:00
Assignment 6: Evaluating Machine Learning Model Performance (PM-06)
Practical AI Skills & Hands-On Implementation (Module 2)

📘 Lesson Summary:

This lesson covers the installation and configuration of essential AI development tools. Learners will learn how to set up Python, IDEs, libraries, and system environments needed for AI and machine learning projects.

Lesson 1: Setting Up AI Development Tools & Environments (PM-04)

To build AI models, developers must set up a proper development environment. Practical Module PM-04 focuses on installing, configuring, and managing the tools required for AI development. The environment must support Python programming, machine learning libraries, and data processing tools.

This lesson prepares learners to create a clean, functional workspace for their AI tasks.

⭐ 1. Purpose of PM-04

The PM-04 practical module teaches learners to:

  • Install AI-related software
  • Configure development tools
  • Set up Python and required libraries
  • Use IDEs for coding
  • Create a stable environment for ML projects
  • Understand version control basics
  • Document the setup process in a logbook

These skills form the foundation for all AI coding tasks.

⭐ 2. Essential Tools for AI Development

a) Python

Python is the primary language used in AI due to its simplicity and powerful libraries.

Learners must install:

  • Python 3.x
  • Pip (package manager)

b) IDE or Code Editor

Examples include:

  • PyCharm
  • Visual Studio Code
  • Jupyter Notebook
  • Google Colab

These tools help write and manage AI code.

c) Machine Learning Libraries

After Python is installed, learners must configure ML libraries such as:

  • NumPy
  • Pandas
  • Scikit-Learn
  • Matplotlib
  • TensorFlow or PyTorch

These packages allow developers to process data, build models, and perform training.

d) Virtual Environments

Virtual environments help isolate Python projects so each project has its own libraries.

Commands include:

 
python -m venv myenv
myenv\Scripts\activate (Windows)
source myenv/bin/activate (Mac/Linux

⭐ 3. System Preparation

Learners must ensure the system meets basic AI requirements:

  • Enough storage space
  • Reliable internet connection
  • Updated operating system
  • Ability to install Python and libraries

System readiness is recorded in the practical logbook.

⭐ 4. Practical Activities in PM-04

Learners complete tasks such as:

  • Installing Python
  • Installing an IDE
  • Creating a virtual environment
  • Installing at least three machine learning libraries
  • Running a test script
  • Documenting all installations and versions

These tasks ensure learners can begin building AI models in later modules.

⭐ 5. Importance in the Workplace

AI developers must be able to:

  • Set up systems from scratch
  • Install necessary libraries
  • Troubleshoot errors
  • Work with multiple tools
  • Ensure reproducibility
  • Follow standard development workflows

PM-04 prepares learners for this real-world responsibility.

 

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