Skip to content
Ed-Tech Africa

Individual Course

AI & Machine Learning

Empowering machines to learn, adapt, and solve complex problems.

Empowering machines to learn, adapt, and solve complex problems.

What is AI and machine learning?

Artificial intelligence refers to systems that perform tasks associated with human intelligence, such as learning, reasoning, planning, recognition, and problem solving.

Machine learning is a branch of AI where systems improve from data and experience. Instead of being manually programmed for every case, models identify patterns and use them to make predictions or decisions.

Core concepts covered

The course introduces AI in a practical way, connecting theory to examples learners can understand and build on.

  • Planning, learning, reasoning, and problem solving
  • Data preparation and pattern recognition
  • Model training, testing, and evaluation
  • Responsible use and clear communication of AI outputs

Everyday examples

Learners connect classroom concepts to services and products they already recognize.

  • Smart assistants and automation
  • Recommendation systems
  • Image and face recognition
  • Wearables and connected devices
  • Email spam filtering

Who it is for

This course is suitable for beginners, career switchers, working professionals, entrepreneurs, and teams that want guided practical training with real tools and clear outcomes.

Tools and technologies

  • Python
  • data preparation
  • machine learning workflows
  • notebooks
  • model evaluation
  • practical AI tools

Learning outcomes

  • Understand AI concepts
  • prepare data
  • train simple models
  • evaluate model behavior
  • and build applied prototypes.

Career paths

  • Robotics Scientist
  • AI Data Analyst
  • Software Engineer
  • Business Intelligence Developer
  • Big Data Engineer
  • Research Scientist

Practical applications

  • Maps
  • smart assistants
  • recommendation systems
  • facial recognition
  • spam filtering
  • automation
  • predictive analytics

Examples and proof points

  • Google Maps routing
  • Snapchat filters
  • self-driving systems
  • wearables
  • email automation
  • product recommendations

Project work

  • Build a prediction model
  • create a classification demo
  • prepare a data notebook
  • and explain model results to a non-technical audience
Admissions