Introduction to TensorFlow in Python

It's hands-on and pitched at people comfortable in Python who specifically want TensorFlow, so the framework choice is really the deciding factor. TensorFlow and PyTorch cover much the same ground; you'd pick this if your workplace, team or target job runs on TensorFlow, and take a PyTorch course otherwise. Learning one makes the other far easier later, so the choice is about fit today, not a lifelong commitment.

One practical note that's easy to trip on: it covers TensorFlow 2.6, so check for API differences against the current version before relying on specific calls. DataCamp is subscription-based (about $14/month billed annually, first chapter free), with a completion certificate that marks what you covered, not a credential. The core TensorFlow concepts carry across versions even as the API shifts (as of 2026).

Introduction to TensorFlow in Python covers TensorFlow 2.6 from the ground up: tensors and basic operations, linear regression for predicting house prices, building neural networks with dense layers and activation functions, and finally combining TensorFlow with the high-level Keras and Estimators APIs.