In the process of modeling, we usually feed the data features to a ML method or algorithm and train the model, typically to optimize a specific cost function, in most cases with the objective of reducing errors and generalizing the representations learned from the data.

Depending upon the dataset and project requirements, we apply one or a combination of different ML techniques. These can include supervised techniques such as classification or regression, unsupervised techniques such as clustering, or even a hybrid approach combining different techniques (as discussed earlier in the ML techniques sections).

Modeling is usually an iterative process, and we often leverage multiple algorithms or methods and choose the best model, based on model evaluation performance metrics. Since this is a book about transfer learning, we will mostly be building deep learning based models in subsequent chapters, but the basic principles of modeling are quite similar to ML models.