What is Data? (Structured vs Unstructured)

The Concept of Features and Labels

Training, Testing, and Making Predictions

Accuracy, Errors, and Improvement

Supervised Learning

Unsupervised Learning

Reinforcement Learning

ML in Healthcare

ML in Finance & Business

ML in Agriculture & Environment

ML in Social Media & Marketing

ML in Africa (Fintech, AgriTech, EdTech examples)

Overview of ML Workflow

ML Tools: Teachable Machine, Google Colab, AutoML

No-Code Demo: Build Your First ML Model Using Teachable Machine

Exploring Datasets (Kaggle, UCI)

Bias in ML Models

Data Privacy & Consent

The Problem of Explainability

Responsible Use of ML

Career Roles in ML

What to Learn Next (Python, Math, Libraries)