Getting started with TensorFlow 2

Imperial College London introduces TensorFlow before using its sequential model API. Validation, regularization and callbacks lead into saving and loading models, then a capstone. The course provides a structured implementation path with explicit attention to evaluating and reusing a trained model.

What you will learn

• Construct a model with a sequential API.
• Use validation and regularization during training.
• Save and reload a trained model for later use.

Who this suits

Intermediate learners; detailed prerequisites are not listed in the published course facts. Preview the expected Python and neural-network background.

Before you enroll

Check the TensorFlow and Keras versions used by the notebooks. APIs and model formats can change; reproduce a small example before investing time in a larger project.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Train and save a small model, then reload it in a fresh process. Compare predictions and record the dependencies needed to reproduce the result.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.