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.
0/3
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.
0/3
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.
0/3
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.
0/3
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.
0/3
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.
0/3
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 introduces the essential data skills needed before building AI models, including collecting, cleaning, preparing, and describing datasets. Learners will understand why proper data preparation is critical in machine learning projects.

Lesson 1: AI Data Handling & Preparation (PM-02)

AI systems rely on data and the quality of that data determines how well your model performs. Practical Module PM-02 focuses on the skills required to collect, clean, prepare, and understand datasets before they are used for training machine learning algorithms.

This is one of the most important stages of AI development because poor data = poor results.
In this lesson, you will learn the core responsibilities involved in preparing data for AI tasks.

⭐ 1. Purpose of PM-02

The purpose of this Practical Module is to help learners develop the ability to:

  • Collect data from various sources
  • Clean and preprocess datasets
  • Handle missing or incorrect data
  • Describe data using basic statistics
  • Prepare structured datasets for AI training

These skills form the foundation of any real AI project and must be mastered before working with models or algorithms.

⭐ 2. Responsibilities of the Learner (from PM-02)

Learners are expected to:

  • Complete all data-related tasks in the practical workbook
  • Use the correct tools for data cleaning (Excel, Python, or CSV editors)
  • Understand and apply instructor/mentor feedback
  • Maintain accurate logbook entries
  • Handle data ethically and responsibly

These steps help build confidence when working with datasets.

⭐ 3. Key Skills in Data Preparation

PM-02 focuses on five core data skills:

a) Data Collection

Learners must identify sources of relevant data such as spreadsheets, databases, surveys, APIs, or open-source datasets.

b) Data Cleaning

This involves removing mistakes, duplicates, missing values, invalid entries, and formatting errors.

c) Data Transformation

Changing data into the right format, such as converting text to numerical categories or normalising values.

d) Data Structuring

Organising data into rows and columns appropriate for AI and machine learning algorithms.

e) Dataset Description

Producing simple summaries such as:

  • mean
  • minimum
  • maximum
  • counts
  • unique values

These summaries help you understand the dataset before training an AI model.

⭐ 4. Practical Activities in PM-02

Learners will complete practical tasks such as:

  • Importing raw data
  • Inspecting and cleaning the dataset
  • Handling missing or incorrect values
  • Structuring data inside spreadsheets or databases
  • Recording each activity in the logbook

These tasks simulate real work done by data science and machine learning teams.

⭐ 5. Workplace Relevance

Companies depend on clean, reliable data to make decisions.
Poor data preparation leads to:

  • incorrect predictions
  • biased results
  • weak model performance
  • wasted time and resources

By mastering PM-02, learners become ready to support real-world AI projects.

Previous
Next
Scroll to Top