This practical course examines the machine-learning pipeline using Keras and Python, from environment setup and data preparation through model training and evaluation. It provides applied context for regression, binary classification, multiclass classification, and deep-learning workflows.

The course also explores model-building approaches in Keras, including the Sequential and Functional APIs, convolutional neural networks, pooling, dropout, and techniques for preparing data correctly for training and testing.