Applied AI & Data Analytics Fundamentals

Introduction to AI and Machine Learning

A practical introduction to artificial intelligence, machine learning, and deep learning, including how to formulate a problem before selecting a model.

Data Quality and Preparation

An introduction to data types, missing values, outliers, preprocessing, and the importance of preventing information leakage between datasets.

Training, Validation and Testing

Explains the roles of training, validation, and test datasets and how to use them correctly for realistic model evaluation.

Model Evaluation and Overfitting

Covers Accuracy, Precision, Recall, F1, MAE, RMSE, and related metrics while explaining overfitting and methods for detecting it.

Model Deployment and Monitoring

Explains how to turn a trained model into an operational service and monitor inference latency, failures, and post-deployment data changes.

Responsible AI, Uncertainty and Drift

Discusses model reliability, uncertainty, data drift, operational risk monitoring, and cases requiring human intervention.