Google: Introduction to AI

Lesson 1 • AI's Societal and Economic Impact

Examines how AI reshapes industries, labour markets, and daily life. Motivates learners by connecting technical concepts to tangible outcomes.

Lesson 2 • Defining Artificial Intelligence

Clarifies what AI means technically and colloquially, separating fact from fiction. Grounds the chapter by anchoring all subsequent concepts to a shared definition.

Lesson 3 • Key AI Terminology

Introduces essential vocabulary used throughout the course, including algorithms, models, and data. Ensures students can read and discuss AI content without confusion.

Lesson 1 • Types and Sources of Data

Distinguishes structured, unstructured, and semi-structured data and their origins. Sets the stage for understanding what AI systems actually consume.

Lesson 2 • Data Quality and Bias

Examines how incomplete, imbalanced, or biased data corrupts model outputs. Connects data integrity directly to fairness and reliability outcomes.

Lesson 3 • Data Preprocessing Techniques

Covers normalisation, encoding, and feature engineering as preparation steps before training. Demonstrates how raw data is transformed into model-ready inputs.

Lesson 1 • Choosing the Right Learning Approach

Provides a decision framework for selecting among supervised, unsupervised, and reinforcement learning. Synthesises the chapter by applying all three paradigms to realistic scenarios.

Lesson 2 • Reinforcement Learning Basics

Introduces agents, environments, rewards, and policies as the core RL framework. Positions RL as a distinct paradigm suited to sequential decision-making tasks.

Lesson 3 • Unsupervised Learning Fundamentals

Covers how models find hidden structure in unlabeled data through clustering and dimensionality reduction. Contrasts with supervised learning to sharpen conceptual boundaries.

Lesson 1 • Backpropagation and Gradient Descent

Explains how errors propagate backward to update weights and reduce loss. Completes the training loop by linking error measurement to parameter adjustment.

Lesson 2 • Loss Functions and Error Measurement

Defines loss functions as quantitative measures of prediction error. Connects error measurement to the optimisation goal of training.

Lesson 3 • Neurons and Network Architecture

Explains artificial neurons, layers, and how they connect to form a network. Provides the structural vocabulary needed to understand learning mechanics.

Lesson 1 • Text Representation for AI

Covers tokenisation, embeddings, and vector representations that convert text to numbers. Establishes the preprocessing foundation all NLP models depend on.

Lesson 2 • Transformer Architecture Overview

Explains attention mechanisms and the transformer model that powers modern NLP. Positions transformers as the architectural shift enabling large language models.

Lesson 3 • Core NLP Tasks and Applications

Surveys sentiment analysis, named entity recognition, translation, and summarisation. Connects abstract NLP concepts to concrete business and consumer use cases.

Lesson 1 • Multimodal AI Systems

Examines models that combine text, image, audio, and other data types simultaneously. Positions multimodal AI as the frontier connecting vision and language capabilities.

Lesson 2 • Vision Tasks Beyond Classification

Introduces object detection, segmentation, and pose estimation as advanced vision tasks. Expands student understanding beyond simple image labeling.

Lesson 3 • How Machines See Images

Covers pixel representation, colour channels, and how raw images become model inputs. Builds the perceptual foundation before introducing convolutional processing.

Lesson 1 • Transparency and Explainability

Covers black-box limitations and explainability methods that make AI decisions interpretable. Connects transparency to trust, accountability, and regulatory expectations.

Lesson 2 • Responsible AI Frameworks

Surveys organisational and industry-level principles for ethical AI governance. Equips students to apply responsible AI guidelines within their own organisations.

Lesson 3 • Bias and Fairness in AI

Analyses how algorithmic bias emerges and its disproportionate impact on marginalised groups. Motivates fairness as a technical and moral design requirement.

Lesson 1 • Measuring Business Impact of AI

Establishes methods for quantifying AI's return on investment and organisational value. Closes the chapter by linking technical outcomes to strategic business results.

Lesson 2 • Selecting Models and Tools

Guides selection among pre-built APIs, fine-tuned models, and custom training approaches. Connects prior learning to practical tooling decisions based on constraints.

Lesson 3 • Building and Iterating on AI Solutions

Covers prototyping, experimentation, and iterative improvement cycles for AI systems. Reinforces that AI development is empirical and requires structured iteration.

Lesson 4 • Scoping and Framing AI Projects

Teaches how to translate a business problem into a well-defined AI task with measurable goals. Anchors the applied chapter by establishing project clarity before any technical work.