AI Fundamentals For Beginners

Course Units/Overview for AI Fundamentals For Beginners

Week 1: Understanding Artificial Intelligence.

Lesson 1.1: What Is Artificial Intelligence?

• Definition of AI
• Examples of AI in everyday life

Lesson 1.2: The AI Hierarchy

• AI vs Machine Learning vs Deep Learning
• Visual hierarchy explanation

Lesson 1.3: Where Does Generative AI Fit?

• Generative AI as part of Deep Learning
• Introduction to LLMs

Lesson 1.4: Generative AI Tools Widely Used Today

• ChatGPT, Google Bard, DeepSeek, Elevenlabs
• How these tools use LLMs

Lesson 1.5: The Future of AI (2026 View)

• AI as a platform
• Increasing AI adoption across industries

Week 2: Machine Learning Explained Simply

Lesson 2.1: What Is Machine Learning?

• Learning from data instead of rules

Lesson 2.2: Training Data and Models

• What is training data?
• What is a model?

Lesson 2.3: Making Predictions with Machine Learning

• Using trained models on unseen data

Lesson 2.4: Business Example: Sales Forecasting

• Bata sales data predicting Umoja shoe sales

Lesson 2.5: Learning From Mistakes- closing the Gap- time-saving

• Prediction vs actual outcome
• Improving the model

Week 3: Types of Machine Learning – Supervised and Unsupervised

Lesson 3.1: Introduction to Types of Machine Learning

• Why do different learning types exist

Lesson 3.2: Supervised Learning Explained

• Labeled data
• Classification and prediction

Lesson 3.3: Supervised Learning Example

• Beauty product returns (picked vs delivered)

Lesson 3.4: Unsupervised Learning Explained

• Unlabeled data
• Pattern discovery

Lesson 3.5: Clustering Example

• Employee income vs tenure (G1 and G2)

Lesson 3.6: Key Differences Between Supervised and Unsupervised Learning

• Labels
• Feedback and error correction

Week 4: Deep Learning and Neural Networks

Lesson 4.1: Introduction to Deep Learning

• Why deep learning is different from traditional ML

Lesson 4.2: Neural Networks Explained Simply

• Neurons, nodes, and layers

Lesson 4.3: Why More Layers Matter

• Model power and complexity

Lesson 4.4: Semi-Supervised Learning

• Combining labelled and unlabeled data

Lesson 4.5: Real-World Example: Fraud Detection

• Banking transactions (5% labelled, 95% unlabeled)

Week 5: Discriminative vs Generative AI Models

Lesson 5.1: Two Ways AI Learns

• Classification vs creation

Lesson 5.2: Discriminative Models

• Learning from labels
• Examples: fraud detection, cats vs dogs

Lesson 5.3: Generative Models

• Learning patterns instead of labels

Lesson 5.4: Generating New Content

• Example: generating a dog image

Lesson 5.5: Comparing Outputs

• Numbers and probabilities vs text, images, audio

Week 6: Generative AI, LLMs, and the Future

Lesson 6.1: What Are Large Language Models (LLMs)?

• High-level explanation

Lesson 6.2: Types of Generative AI Models

• Text-to-text
• Text-to-image
• Text-to-audio

Lesson 6.3: Multimodal AI

• Combining text, images, and voice

Lesson 6.4: Agentic AI

• AI systems that plan and act

Lesson 6.5: Ethics and Responsible AI Use

• Bias, misuse, and limitations

Lesson 6.6: AI in the Real World

• Future of work and everyday applications