Artificial Intelligence for Beginners Course

Lesson 1 • AI in Everyday Life

Maps AI applications across industries including healthcare, finance, and retail. Grounds abstract concepts in familiar, observable experiences.

Lesson 2 • A Brief History of AI

Traces AI from early symbolic systems to modern neural networks. Provides historical context that explains why current approaches dominate.

Lesson 3 • Core AI Problem Types

Introduces classification, regression, clustering, and generation as the four fundamental problem categories AI solves. Frames all later techniques within these types.

Lesson 1 • Data Collection and Storage

Covers methods for gathering data and storing it at scale for AI pipelines. Links data infrastructure decisions to downstream model quality.

Lesson 2 • Feature Engineering Basics

Introduces the process of transforming raw data into meaningful inputs for AI models. Shows how feature selection directly impacts model accuracy.

Lesson 3 • How AI Learns from Data

Explains training, validation, and test splits and why each matters. Introduces the concept of a model as a mathematical function fitted to data.

Lesson 4 • Data Quality and Bias

Examines how poor data quality and biased datasets degrade AI performance and fairness. Prepares students to critically evaluate data before use.

Lesson 1 • Unsupervised Learning Explained

Explores finding hidden structure in unlabeled data through clustering and dimensionality reduction. Connects to the clustering problem type introduced earlier.

Lesson 2 • Supervised Learning Explained

Covers learning from labeled examples to predict outputs on new data. Anchors the concept with classification and regression tasks from Chapter 1.

Lesson 3 • Reinforcement Learning Basics

Introduces agents learning through reward and penalty signals in an environment. Distinguishes this paradigm from supervised and unsupervised approaches.

Lesson 4 • Choosing the Right Algorithm

Provides a decision framework for selecting algorithms based on data type, size, and problem goal. Synthesises all three learning paradigms into a practical guide.

Lesson 1 • Layers and Network Architecture

Explains input, hidden, and output layers and how depth creates representational power. Connects layer depth to the ability to solve harder problems.

Lesson 2 • Training Neural Networks

Covers forward pass, loss calculation, backpropagation, and gradient descent intuitively. Shows how networks improve iteratively through training cycles.

Lesson 3 • How a Neuron Works

Breaks down the artificial neuron as a weighted sum followed by an activation function. Builds the atomic unit of understanding for all neural network architectures.

Lesson 4 • Convolutional Neural Networks

Introduces CNNs as specialised networks for image and spatial data processing. Demonstrates how filters detect edges, textures, and objects hierarchically.

Lesson 1 • Representing Text as Numbers

Introduces bag-of-words, TF-IDF, and word embeddings as methods to convert text into numerical form. Bridges the gap between language and machine-readable input.

Lesson 2 • Text Preprocessing Techniques

Covers tokenisation, stopword removal, stemming, and lemmatisation as foundational text cleaning steps. Prepares raw text for downstream NLP model input.

Lesson 3 • Building a Simple NLP Pipeline

Walks through assembling a complete text classification pipeline from raw data to prediction. Synthesises all section concepts into an end-to-end applied workflow.

Lesson 4 • Core NLP Tasks

Surveys sentiment analysis, named entity recognition, machine translation, and summarisation. Maps each task to practical business and product applications.

Lesson 1 • Object Detection and Localisation

Distinguishes classification from detection and introduces bounding box prediction. Expands student understanding beyond simple image labeling tasks.

Lesson 2 • How Computers See Images

Explains pixel representation, colour channels, and image tensors as the raw input to vision models. Grounds visual AI in the data structures computers actually process.

Lesson 3 • Image Segmentation Overview

Introduces semantic and instance segmentation as pixel-level understanding tasks. Connects to medical imaging, autonomous driving, and satellite analysis use cases.

Lesson 4 • Transfer Learning in Vision

Explains how pretrained vision models are fine-tuned for new tasks with limited data. Demonstrates the practical efficiency gains from reusing learned representations.

Lesson 1 • Prompt Engineering Techniques

Teaches zero-shot, few-shot, chain-of-thought, and role prompting strategies. Enables students to extract reliable, high-quality outputs from LLMs without retraining.

Lesson 2 • How Large Language Models Work

Explains pretraining on massive text corpora and fine-tuning for specific tasks. Builds on transformer knowledge from Chapter 5 to explain LLM scale effects.

Lesson 3 • Limitations and Hallucinations

Examines factual errors, hallucinations, and reliability issues in generative AI outputs. Equips students to use generative tools responsibly with appropriate verification.

Lesson 4 • Generative Image and Multimodal AI

Covers diffusion models and multimodal systems that process text and images together. Extends generative AI understanding beyond text to visual and cross-modal outputs.

Lesson 1 • Bias and Fairness in AI

Examines how training data and model design introduce bias that leads to unfair outcomes. Connects to data quality concepts from Chapter 2 with an ethical lens.

Lesson 2 • Accountability and Governance

Defines roles, responsibilities, and oversight structures for AI systems in organisations. Prepares students to participate in or advocate for responsible AI governance.

Lesson 3 • Privacy and Data Rights

Covers data minimisation, consent, anonymisation, and individual rights over personal data. Frames privacy as a design requirement, not an afterthought.

Lesson 4 • Responsible AI in Practice

Applies ethical principles to real deployment scenarios through case studies and checklists. Synthesises the chapter into actionable habits for everyday AI work.