AI Fundamentals Course

Lesson 1 · Video

AI Fundamentals Course Intro

Lesson 1 · Pdf

AI Fundamentals Course Textbook Sample

Sample Course Textbook (Only Module 1 Included)

Lesson 2 · Video

Module 1 AI Concepts & Capabilities

Lesson 3 · Video

What is AI?

This lesson introduces the foundational concepts of Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI. Learners explore how these technologies relate to one another, examine key milestones in AI history, and understand both the strengths and limitations of modern AI systems. Real-world examples and practical distinctions prepare students for future lessons and certification exam concepts.

Lesson 4 · Video

Training vs Inference

This lesson explains the two major phases of the AI lifecycle: training and inference. Students learn how models are built using data and compute resources during training, and how trained models are later used to make predictions in production environments. Real-world examples such as spam classification help reinforce the operational differences between these phases.

Lesson 5 · Video

Learning Paradigms

This lesson explores the three primary machine learning paradigms: supervised learning, unsupervised learning, and reinforcement learning. Students discover how machines learn from labeled data, uncover hidden patterns, and improve through rewards and feedback. Everyday applications such as spam filters, customer segmentation, and GPS optimization are used to connect theory to practice.

Lesson 6 · Video

Neural Networks at a Glance

This lesson introduces the core structure and functionality of neural networks, the technology behind modern deep learning systems. Learners examine neurons, layers, weights, activation functions, and the process of backpropagation. The lesson also compares shallow and deep networks while explaining why depth gives neural networks their powerful representational capabilities.

Lesson 7 · Video

LLM & Embedding

This lesson provides an introduction to Large Language Models (LLMs), tokens, context windows, and embeddings. Students learn how modern AI systems process and generate language, how embeddings represent semantic meaning, and how techniques like semantic search operate. The lesson also introduces prompt engineering concepts and security risks such as prompt injection attacks.

Lesson 8 · Video

Model Evaluation Metrics

This lesson focuses on how AI and machine learning models are evaluated and measured. Students learn key metrics including accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrices. The lesson also introduces important concepts such as overfitting, underfitting, and the bias-variance tradeoff, helping learners understand how to assess model reliability and performance.

Lesson 9 · Video

Common AI Applications & Limitations

This lesson examines how AI is applied across industries including healthcare, cybersecurity, finance, retail, and entertainment. Students explore applications such as computer vision, natural language processing, recommender systems, forecasting, and anomaly detection. The lesson also highlights important AI limitations including hallucinations, dataset shifts, and interpretability challenges, reinforcing the importance of responsible AI usage

Lesson 10 · Assessment

AI Fundamentals Module 1 Quiz

Lesson 13 · Video

Module 2: Data Stewardship & Ethics

Lesson 14 · Video

Data Lifecycle: Collection To Deletion

This lesson introduces the complete AI data lifecycle and explains how data moves from initial collection through storage, labeling, preprocessing, training, archival, and secure deletion. Learners explore the responsibilities of key stakeholders, common risks at each stage, and the governance practices that help organizations manage data responsibly. Understanding the data lifecycle provides the foundation for privacy, security, compliance, and trustworthy AI development.

Lesson 15 · Video

Sources of Bias & Fairness Basics

This lesson introduces the concept of bias in AI systems and explains how bias can emerge during data collection, labeling, measurement, and model development. Learners explore major bias categories including sampling bias, label bias, measurement bias, and proxy bias. The lesson also provides an introduction to AI fairness, demographic parity, equalized odds, and the challenges organizations face when balancing fairness, performance, and business objectives.

Lesson 16 · Video

Detecting & Mitigating Bias

This lesson focuses on practical methods used to identify and reduce bias in AI systems. Learners examine subgroup analysis, fairness metrics, error rate comparisons, and model auditing techniques used to uncover hidden disparities. The lesson also explores mitigation strategies such as relabeling, reweighing, and dataset diversification, helping students understand how organizations build more equitable, trustworthy, and responsible AI solutions.

Lesson 17 · Video

Privacy Basics, PII, Anonymization, DPIA

This lesson introduces core privacy concepts that are essential for modern AI systems. Learners explore Personally Identifiable Information (PII), sensitive data, privacy risk assessment, anonymization, pseudonymization, and re-identification risks. The lesson also explains the purpose and structure of Data Protection Impact Assessments (DPIAs) and demonstrates how privacy-by-design principles help organizations comply with regulations while protecting individuals and maintaining trust.

Lesson 18 · Video

Intro to Differential Privacy & Synthetic Data

This lesson explores two important privacy-enhancing technologies used in AI and data analytics. Learners discover how differential privacy uses mathematical techniques to protect individual contributions within datasets and how synthetic data generates artificial records that preserve useful patterns while reducing privacy exposure. The lesson examines privacy-utility tradeoffs, privacy budgets, data sharing challenges, and the growing role of privacy-preserving technologies in responsible AI development.

Lesson 19 · Video

Data Governance & Lineage

This lesson examines the frameworks and processes that help organizations manage data responsibly throughout its lifecycle. Learners explore the principles of data governance, stakeholder responsibilities, compliance requirements, data quality controls, and risk management practices. The lesson also introduces data lineage, explaining how organizations trace data origins, transformations, and usage to support transparency, auditing, troubleshooting, and trustworthy AI operations.

Lesson 19 · Assessment

AI Fundamentals Module 2 Quiz

Lesson 20 · Video

Module 3: AI Lifecycle & Platforms

Lesson 21 · Video

AI Lifecycle Overview

This lesson introduces the complete AI lifecycle, providing a structured framework for understanding how artificial intelligence systems are designed, developed, deployed, monitored, and maintained. Learners explore the seven major stages of the AI lifecycle, including data collection, model development, validation, deployment, monitoring, retraining, and retirement. The lesson emphasizes that AI is not a one-time project but an ongoing operational process requiring governance, accountability, and continuous improvement. Understanding the AI lifecycle helps learners recognize how organizations build trustworthy, secure, and effective AI systems in real-world environments.

Lesson 22 · Video

Model Registries & Artifact Management

This lesson introduces model registries and artifact management, two essential components of modern AI operations and governance. Learners explore how organizations store, version, track, secure, and manage machine learning models throughout their lifecycle. The lesson examines model binaries, metadata, validation artifacts, provenance records, digital signing, and immutability controls. Students will learn how registries support reproducibility, compliance, collaboration, auditing, and trustworthy AI deployment in enterprise environments.

Lesson 22 · Video

AI Supply Chain Threats

Lesson 23 · Video

Deployment Patterns

This lesson explores the most common deployment patterns used to deliver AI models into production environments. Learners examine batch inference, real-time inference, edge deployment, and hybrid architectures, along with the tradeoffs associated with each approach. The lesson highlights considerations such as latency, scalability, reliability, cost, and security. Students will gain a practical understanding of how organizations choose deployment strategies based on business requirements and operational constraints, helping bridge the gap between model development and real-world AI operations.

Lesson 24 · Video

APIs and Inference Gateways

This lesson introduces APIs and inference gateways, the technologies that enable applications to interact with AI models in production environments. Learners explore how APIs expose AI capabilities to users and systems while gateways provide security, scalability, observability, and traffic management. The lesson examines concepts such as rate limiting, throttling, batching, cold starts, authentication, caching, and monitoring. Students will gain practical knowledge of how organizations operate AI services reliably, securely, and efficiently at enterprise scale.

Lesson 25 · Video

Cloud AI Platforms

This lesson introduces the major cloud AI platforms and the services they provide for building, deploying, managing, and scaling artificial intelligence solutions. Learners explore the capabilities offered by cloud providers, including model training, inference services, storage, security, monitoring, and governance. The lesson examines the benefits and challenges of cloud-based AI while highlighting how organizations use cloud platforms to accelerate innovation, reduce infrastructure complexity, and support modern AI operations.

Lesson 26 · Video

Monitoring & Observability

This lesson introduces monitoring and observability practices used to maintain AI systems after deployment. Learners explore performance monitoring, quality monitoring, drift detection, alerting, and operational response processes. The lesson explains how organizations track system health, detect degradation before it impacts users, and respond to incidents efficiently. Students will learn why monitoring is a critical component of trustworthy AI operations and how observability provides the visibility required to manage AI systems at scale.

Lesson 27 · Video

Documentation Model Cards & Factsheets

This lesson introduces documentation practices that support transparency, accountability, governance, and trust in AI systems. Learners explore model cards and factsheets, two widely adopted frameworks used to communicate how AI models are developed, evaluated, deployed, and governed. The lesson examines intended use cases, limitations, performance reporting, provenance, risk classification, and responsible AI disclosures. Students will learn how documentation supports audits, compliance, stakeholder communication, and trustworthy AI operations throughout the model lifecycle.

Lesson 27 · Assessment

AI Fundamentals Module 3 Quiz

Lesson 28 · Video

Module 4: AI Risk & Security Fundamentals

Lesson 29 · Video

AI Threats Overview

This lesson introduces the modern AI threat landscape and provides a foundational understanding of the most important risks facing AI systems today. Learners explore hallucinations, prompt injection, data poisoning, evasion attacks, and model extraction, while examining how these threats impact trust, security, privacy, and business operations. The lesson also introduces a defense framework built around prevention, detection, and mitigation, providing the foundation for the remaining AI security topics in Module 4.

Lesson 30 · Video

Adversarial Examples

This lesson introduces adversarial examples, one of the most important concepts in AI security. Learners explore how tiny, often invisible modifications to data can cause machine learning models to make incorrect predictions. The lesson explains why adversarial examples work, examines visual attack scenarios, and introduces foundational robustness concepts. Students will gain an intuitive understanding of AI vulnerabilities and the defensive techniques used to make models more resilient against manipulation.

Lesson 31 · Video

Data Poisoning & Integrity Attacks

This lesson introduces data poisoning and integrity attacks, which target AI systems during the training process rather than after deployment. Learners explore how attackers manipulate training data to influence model behavior, create hidden vulnerabilities, or reduce system accuracy. The lesson examines poisoning techniques, backdoor attacks, data integrity risks, and defensive controls used to protect AI training pipelines. Students will gain a practical understanding of why trusted data is essential for trustworthy AI.

Lesson 32 · Video

Model Privacy Risks (Membership Inference)

This lesson introduces membership inference attacks and other privacy risks that can emerge when AI systems unintentionally reveal information about their training data. Learners explore how attackers analyze model outputs to determine whether specific records were used during training, why this creates privacy concerns, and the safeguards organizations use to reduce exposure. The lesson also introduces privacy-preserving techniques such as differential privacy and responsible model design practices that help protect sensitive information.

Lesson 33 · Video

Secrets, API Keys & Credential Hygiene

This lesson introduces secrets management and credential hygiene, two critical foundations of AI security. Learners explore API keys, tokens, passwords, service accounts, and other credentials that enable AI systems to access models, data, cloud services, and external tools. The lesson examines common mistakes such as hardcoding secrets, credential sharing, and excessive permissions, while introducing best practices including secret vaults, rotation, least privilege, and secure storage. Students will learn how poor credential management can create significant security risks and how organizations protect sensitive access mechanisms.

Lesson 34 · Video

Operational Security Controls

This lesson introduces the operational security controls used to protect AI systems throughout their lifecycle. Learners explore access control, logging, monitoring, change management, security reviews, segmentation, incident response, and defense-in-depth strategies. The lesson emphasizes that secure AI is not achieved through a single technology but through multiple layers of operational safeguards working together. Students will gain an understanding of how organizations build resilient AI environments that remain secure, reliable, and trustworthy over time.

Lesson 35 · Video

Monitoring for Security Incidents

This lesson introduces the processes and technologies organizations use to monitor AI environments for potential security incidents. Learners explore logging, alerting, security analytics, anomaly detection, indicators of compromise, and incident response workflows. The lesson explains how continuous monitoring helps organizations identify attacks, investigate suspicious activity, and respond quickly to emerging threats. Students will gain an understanding of how monitoring serves as a critical layer of defense in modern AI security programs.

Lesson 35 · Assessment

AI Fundamentals Module 4 Quiz

Lesson 36 · Video

Module 5: Governance & Professional Practices

Lesson 37 · Video

Governance Roles & Accountability

This lesson introduces governance as the foundation of responsible AI adoption. Learners explore how organizations establish accountability, define oversight responsibilities, and assign roles across AI initiatives. The lesson examines the RACI framework, board-level governance, project-level governance, and the importance of clear ownership throughout the AI lifecycle. Students will learn how effective governance reduces risk, improves transparency, and supports trustworthy AI operations.

Lesson 38 · Video

NIST AI RMF & Framework Mapping

This lesson introduces the National Institute of Standards and Technology AI Risk Management Framework (AI RMF), one of the most influential frameworks for managing AI risk. Learners explore the framework's four core functions—Govern, Map, Measure, and Manage—and examine how organizations use structured frameworks to identify, assess, and mitigate AI-related risks. The lesson also introduces framework mapping, helping students understand how governance activities align with security, privacy, compliance, and operational controls throughout the AI lifecycle

Lesson 39 · Video

EU AI Act & Global Regulation Trends

This lesson introduces the regulatory landscape shaping the future of Artificial Intelligence. Learners explore the EU AI Act, one of the world's first comprehensive AI regulations, and examine how governments and regulatory bodies are approaching AI governance globally. The lesson covers risk-based regulation, prohibited AI systems, high-risk AI applications, compliance obligations, and emerging international trends. Students will gain an understanding of how regulation influences AI development, deployment, and organizational governance practices.

Lesson 40 · Video

Model Documentation & Audit Evidence

This lesson explores how organizations document AI systems and maintain audit evidence to support governance, compliance, and accountability. Learners examine documentation practices throughout the AI lifecycle, including model cards, factsheets, testing records, approval workflows, risk assessments, and decision logs. The lesson explains how audit evidence demonstrates that governance processes were followed and why documentation is essential for transparency, regulatory compliance, and trustworthy AI operations.

Lesson 41 · Video

Ethics & Responsible AI

This lesson explores the ethical principles that guide responsible AI development and deployment. Learners examine fairness, transparency, accountability, privacy, safety, human oversight, and societal impact. The lesson explains why ethical considerations extend beyond legal compliance and how organizations use responsible AI practices to build trust and reduce risk. Students will gain an understanding of the role ethics plays in creating AI systems that serve individuals, organizations, and society responsibly.

Lesson 41 · Assessment

AI Fundamentals Module 5 Quiz

Lesson 42 · Assessment

AI Fundamentals Final Exam