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 explains the core concepts of machine learning from a practical perspective. Learners will understand training data, testing data, model pipelines, and how algorithms learn from structured datasets.

Lesson 1: Machine Learning Fundamentals (PM-03)

Machine Learning (ML) is a core part of Artificial Intelligence. It enables computers to learn patterns from data and make predictions without being explicitly programmed. Practical Module PM-03 introduces the structured workflow needed to prepare, train, and evaluate machine learning models.

This topic focuses on understanding datasets, training ML models, evaluating performance, and building confidence in real-world AI tasks.

⭐ 1. Purpose of PM-03

PM-03 helps learners develop the ability to:

  • Prepare datasets for ML tasks
  • Train simple machine learning models
  • Evaluate model accuracy
  • Understand how algorithms learn
  • Build basic AI pipelines

This is the foundation for more advanced AI development covered in later topics.

⭐ 2. Key Concepts in Machine Learning

a) Training Data

The data used to “teach” the ML model.

b) Testing Data

Used to check if the model has learned correctly.

c) Features

The inputs used by the model (e.g., age, salary, score).

d) Labels / Target

The value the model tries to predict (e.g., spam/not spam).

e) Machine Learning Models

Algorithms that identify patterns inside the data.

⭐ 3. Machine Learning Workflow

PM-03 teaches a simple but essential ML workflow:

Step 1: Prepare the Dataset

Clean and structure the data for use in model training.

Step 2: Split the Dataset

Typical splits include:

  • 70% training
  • 30% testing
    or
  • 80% training
  • 20% testing
  • Step 3: Train the Model

Use the algorithm to learn from the training data.

Step 4: Test the Model

Evaluate performance using the testing data.

Step 5: Improve the Model

Adjust parameters and repeat the workflow.

⭐ 4. Types of Machine Learning Covered

PM-03 introduces:

✔ Supervised Learning

Training on labelled data (e.g., predicting house prices).

✔ Unsupervised Learning

Finding patterns in unlabelled data (e.g., clustering customers).

✔ Classification Models

Predict categories (e.g., fraud/not fraud).

✔ Regression Models

Predict continuous values (e.g., sales amount).

⭐ 5. Practical Tasks in PM-03

Learners will complete tasks such as:

  • Loading a dataset
  • Splitting into training and testing sets
  • Selecting a simple ML algorithm
  • Training the model
  • Measuring accuracy
  • Recording results in the logbook

These tasks simulate real AI development jobs.

⭐ 6. Workplace Relevance

Machine learning is used across:

  • Finance (fraud detection)
  • Marketing (customer predictions)
  • Healthcare (diagnosis models)
  • Manufacturing (defect detection)

Mastering these fundamentals is essential for all AI careers.

 

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