Artificial Intelligence Fundamentals
For aspiring AI practitioners, use mathematics to update probabilities, trace neural-network calculations, and tune a parameter with gradient descent.
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Artificial Intelligence Fundamentals
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About this course
Designed for learners building foundations in AI. You will connect vectors and matrices with a neural-network pass, update beliefs with Bayes' theorem, and use derivatives to adjust a loss function. At work, you can explain model steps and check assumptions instead of treating outputs as magic. AI systems are built on four mathematical foundations: linear algebra represents data as vectors, matrices, and tensors that flow through neural network layers via matrix multiplication; probability and Bayes' theorem update prior beliefs into posteriors using observed evidence, underpinning classification and uncertainty estimation; and calculus provides the gradient descent mechanism that iteratively adjusts parameters to minimize a loss function. Statistics supplies the tools to summarize distributions and determine whether observed patterns are meaningful rather than random noise. This module walks you through the end-to-end workflow of a supervised learning project: framing the problem, preparing data, training a baseline model, comparing alternatives, and documenting the trade-offs. By the end you will understand how practitioners move from a raw dataset to a defensible predictive model, and how to reason about accuracy, complexity, and cost. This module connects the technical building blocks behind modern AI systems. You will learn how neural networks learn from data, what embeddings and attention actually do, why transformers changed everything, and why large language model (LLM) output must always be verified before it reaches a decision-maker or a customer. Before an AI workflow reaches real users, it needs checks to ensure it is accurate, fair, safe and compliant. This module walks you through the practical steps for evaluating AI outputs, identifying risks like bias and privacy leakage, and setting up lightweight governance that non-technical teams can actually run. You will learn how to design test cases, spot common failure modes, and document decisions so stakeholders trust what you ship. This module teaches you how to transform a vague AI idea into a concrete, bounded project plan. You will learn to define the problem, assess data readiness, set quality gates, estimate costs, and design a rollout path. By the end, you will be able to write a one-page AI project brief that stakeholders can approve or reject with confidence. MLOps extends DevOps by treating data and trained models as first-class versioned artifacts alongside code, because ML systems can fail silently—returning valid HTTP responses while producing quietly degraded predictions due to input distribution shift. MLflow tracks parameters, metrics, and model artifacts across training runs and provides a model registry with auditable stage transitions from Staging to Production, while Docker packages the model, its dependencies, and a FastAPI inference endpoint into an immutable container that runs identically across environments. Responsible AI rests on fairness, accountability, and transparency, and a model can achieve high accuracy while failing all three by faithfully replicating historical discrimination present in its training data. Bias enters pipelines at distinct stages—historical, representational, measurement, aggregation, evaluation, and deployment—each offering specific mitigation opportunities, while fairness metrics such as demographic parity, equal opportunity, equalized odds, and calibration are mathematically incompatible in general, making the choice among them a values-based decision requiring input from affected communities and legal teams. A portfolio-grade capstone moves through six sequential stages—problem definition, data collection and exploration, data preparation, model training and experimentation, evaluation, and deployment with documentation—and demonstrating professional discipline across all six stages carries more weight with hiring teams than sophistication in any single one. The project should have a tangible one-sentence use case, publicly available data, and a scope of roughly 20–40 hours, with honest evaluation that includes a baseline comparison, test-set metrics, and an error analysis identifying whether failures are random or systematic.
What you'll learn
- By the end of this module, you'll be able to apply Bayes' theorem to update a prior probability into a posterior using observed evidence, correctly interpreting counterintuitive results such as low posterior probability despite a high-sensitivity test.
- By the end of this module, you'll be able to trace a forward pass through a single neural network layer by computing a matrix–vector product and adding a bias vector, verifying output shape at each step.
- By the end of this module, you'll be able to implement gradient descent on a scalar loss function by iteratively updating a parameter using the derivative and a chosen learning rate until convergence.
- Frame a prediction task and split data into training, validation, and test sets correctly
- Build a baseline model and evaluate it with appropriate metrics for classification or regression
- Compare multiple models on the same data and articulate the trade-offs between them
- Document a supervised learning experiment so that others can reproduce and audit it
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