TensorFlow
An end-to-end machine learning platform.
TensorFlow is taught here in 3 lessons, running from Tensors and eager execution through to Training, saving and serving. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.
| Track | AI & Intelligent Development |
| Lessons | 3 |
| Level | Beginner |
| Reading time | about 39 minutes |
| Prerequisites | Helpful, but not required: scikit-learn |
Lessons
- Tensors and eager executionImmutable constants, trainable variables, shape and dtype rules, and how tf.function turns Python into a traced graph.
- Building a Keras modelSequential and functional APIs, matching the output layer and loss to the task, and the from_logits setting that silently costs you accuracy.
- Training, saving and servingmodel.fit with callbacks, a custom GradientTape loop, the tf.data pipeline, and exporting a model whose preprocessing travels with it.
More in AI & Intelligent Development
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FAQ
How long does the TensorFlow course take?
It has 3 lessons, about 39 minutes of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for TensorFlow?
Not strictly. It helps to have read scikit-learn first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after TensorFlow?
Continue with PyTorch (3 lessons), the next course in AI & Intelligent Development.
Last refreshed 2026-09-18.