AI and Data Literacy for Business Professionals

Trace the history of ai from the 1950s to the 2020s, highlighting milestones such as Turing, the Dartmouth conference, Unimate, Eliza, backpropagation, ImageNet, Watson, AlphaGo, BERT, and GPT three.

Learn how train, validation, and test datasets drive machine learning: train to learn patterns, validate to fine tune, and test to assess generalization on unseen data.

Explore how overfitting and underfitting relate to bias and variance, cross-validation, and hyperparameter tuning to improve model generalization in business analytics.

Explore essential assessment metrics for classification and regression models, with definitions, strengths, and limitations, to build AI and data literacy for business professionals.

Explore neural networks and deep learning foundations for large language models, reinforcement learning, responsible AI, and rapid fire AI concepts including edge computing, embeddings, and transformer models.

Explore neural networks, deep learning, and large language models as the core of modern ai. See how layered networks learn non-linear data patterns and enable language tasks with GPT.

Explore generative AI, built on deep learning and neural networks, shifting from discriminative data classification to creating new content and enabling creative AI applications.

Reinforcement learning teaches an agent to make decisions by performing actions in an environment and receiving rewards or penalties, enabling autonomous adaptation.

Learn responsible AI through transparency, fairness, privacy, security, accountability, inclusivity, and sustainability, with practical examples from credit scoring, bias mitigation, privacy preserving techniques, data anonymization, and encryption.

Explore ten core ai and data concepts, from structured, unstructured, and semi-structured data to embedding, attention, transformer models, ensemble learning, multimodal ai, rag, edge computing, and federated learning.

Identify and mitigate 12 key cognitive biases and fallacies, including availability heuristic, anchoring, sunk cost, framing, hindsight bias, and recency bias, to improve business decisions.

Review data visualization principles to consume visuals as a business professional, not to create them, and learn common types, use cases, and key critique questions.