Basic AI Course

Lesson 1 • Artificial Intelligence (AI)'s Impact Across Industries

Surveys how artificial intelligence (AI) transforms healthcare, finance, manufacturing, and retail. Motivates learners by connecting theory to tangible professional outcomes.

Lesson 2 • Core Artificial Intelligence (AI) Subfields Overview

Maps the major subfields—machine learning (ML), natural language processing (NLP), computer vision, robotics—and their relationships. Prepares learners to categorise artificial intelligence (AI) applications accurately.

Lesson 3 • Defining Artificial Intelligence (AI)

Establishes a precise, working definition of artificial intelligence (AI) distinct from automation and software. Grounds the chapter by clarifying what artificial intelligence (AI) can and cannot do.

Lesson 1 • Data Collection and Sourcing

Covers primary collection, open datasets, and synthetic data generation methods. Equips learners to source appropriate data for specific artificial intelligence (AI) tasks.

Lesson 2 • Types of Data in Artificial Intelligence (AI)

Distinguishes structured, unstructured, and semi-structured data and their artificial intelligence (AI) use cases. Sets the stage for understanding which algorithms suit which data types.

Lesson 3 • Data Governance and Privacy

Introduces responsible data handling, consent, and privacy-preserving techniques. Ensures learners apply ethical standards when managing artificial intelligence (AI) datasets.

Lesson 4 • Data Preprocessing Essentials

Teaches normalization, encoding, and feature engineering as prerequisites to training. Directly enables learners to prepare datasets for the machine learning (ML) chapter.

Lesson 1 • Model Evaluation and Metrics

Teaches accuracy, precision, recall, F1, and AUC for assessing model quality. Enables learners to judge whether a model meets business requirements.

Lesson 2 • Reinforcement Learning Basics

Introduces agents, environments, rewards, and policies at a conceptual level. Prepares learners to recognise reinforcement learning applications without requiring deep math.

Lesson 3 • Unsupervised Learning Concepts

Covers clustering and dimensionality reduction for unlabeled data exploration. Expands learners' toolkit beyond labeled-data scenarios.

Lesson 4 • Supervised Learning Concepts

Explains regression and classification using labeled data with clear input-output mappings. Builds the primary algorithmic vocabulary used throughout the course.

Lesson 1 • Recurrent Neural Networks and Sequences

Introduces recurrent neural networks and long short-term memory for sequential and time-series data processing. Bridges neural network theory to natural language and forecasting applications.

Lesson 2 • Training Neural Networks

Explains backpropagation, gradient descent, and learning rate as the training engine. Demystifies why neural networks improve with more data and iterations.

Lesson 3 • Anatomy of a Neural Network

Breaks down neurons, layers, weights, and activation functions as building blocks. Provides the structural vocabulary needed for all subsequent deep learning topics.

Lesson 4 • Convolutional Neural Networks

Covers convolutional neural network architecture for image recognition tasks including filters and pooling. Connects deep learning theory to computer vision applications introduced earlier.

Lesson 1 • Large Language Models Overview

Surveys pre-trained large language models, fine-tuning, and prompt engineering for practical natural language processing (NLP) tasks. Prepares learners to leverage large language models without training from scratch.

Lesson 2 • Transformer Architecture Essentials

Explains attention mechanisms and the transformer model that powers modern natural language processing (NLP). Provides the conceptual foundation for understanding large language models.

Lesson 3 • Text Preprocessing Pipeline

Covers tokenization, stemming, lemmatization, and stopword removal as natural language processing (NLP) prerequisites. Prepares raw text for downstream modelling tasks.

Lesson 4 • Core Natural Language Processing (NLP) Tasks

Demonstrates sentiment analysis, named entity recognition, and text classification. Gives learners hands-on exposure to the most common natural language processing (NLP) business applications.

Lesson 1 • Image Data Fundamentals

Covers pixel representation, color spaces, and image formats as vision prerequisites. Ensures learners understand the raw material before applying algorithms.

Lesson 2 • Transfer Learning for Vision

Demonstrates using pre-trained vision models and fine-tuning for custom datasets. Enables learners to build high-performing vision systems with limited data.

Lesson 3 • Image Preprocessing and Augmentation

Teaches resizing, normalization, and augmentation strategies to improve model robustness. Directly applies data preparation skills to the vision domain.

Lesson 4 • Object Detection and Localization

Introduces bounding boxes, anchor-based detectors, and evaluation with intersection over union metrics. Extends image classification to locating multiple objects in a scene.

Lesson 1 • Experiment Tracking and Versioning

Introduces tools and practices for logging experiments, datasets, and model versions. Ensures reproducibility and collaboration across artificial intelligence (AI) development teams.

Lesson 2 • Model Packaging and Serving

Covers serialization formats, REST APIs, and containerization for model deployment. Bridges the gap between a trained model and a production-ready service.

Lesson 3 • Monitoring Models in Production

Teaches data drift detection, performance degradation alerts, and retraining triggers. Ensures deployed models remain accurate and reliable over time.

Lesson 4 • Artificial Intelligence (AI) Project Lifecycle

Maps the stages from problem framing through deployment and maintenance. Gives learners a structured workflow to manage any artificial intelligence (AI) project professionally.

Lesson 1 • Societal and Workforce Impacts

Analyzes artificial intelligence (AI)'s effects on employment, privacy, and power concentration. Develops critical thinking about long-term consequences of artificial intelligence (AI) deployment decisions.

Lesson 2 • Explainability and Transparency

Covers Local Interpretable Model-agnostic Explanations, SHapley Additive exPlanations, and model cards as tools for explaining artificial intelligence (AI) decisions. Enables learners to make black-box models interpretable to stakeholders.

Lesson 3 • Responsible Artificial Intelligence (AI) Frameworks

Surveys industry and regulatory responsible artificial intelligence (AI) principles and governance structures. Prepares learners to align artificial intelligence (AI) projects with organisational and societal standards.

Lesson 4 • Sources of Bias in Artificial Intelligence (AI)

Identifies how historical, measurement, and aggregation biases enter artificial intelligence (AI) systems. Grounds ethical analysis in concrete technical causes rather than abstract concerns.