Objectives
Define and describe the six main domains of Artificial Intelligence and explain real-world applications of each with specific examples.
Objectives
Define generative AI and describe its three main architectures along with their corresponding training methods, using labeled diagrams and examples.
Objectives
Define, compare, and contrast key techniques in data wrangling and data exploration (e.g., managing missing values, normalization, outlier detection, and visualization methods) by applying them to a sample dataset.
Objectives
Define input/output features, interpret scatter diagrams, and explain the role of loss functions in linear regression models, then analyze a simple linear regression example using labeled components.
Objectives
Describe how the K-Nearest Neighbors (KNN) algorithm uses supervised learning and how the K-Means algorithm uses unsupervised learning, then compare their objectives, input requirements, and output interpretations.
Objectives
Use Python and scikit-learn to implement and evaluate machine learning models for linear regression, binary classification, and polynomial regression, achieving reasonable accuracy on a holdout test set for classification tasks and interpreting model coefficients and performance metrics.