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Introduction to TensorFlow for AI, ML and Deep Learning

Intermediate English Professional CertificateFREE
SkillsTensorFlowDeep learningKerasPythonNeural networksComputer vision

Is this course right for you?

Our take
The practical counterpart to theory-first courses: with Laurence Moroney you start building working TensorFlow models almost straight away, rather than deriving the maths behind them.

Good for: Getting hands-on with TensorFlow quickly, after some ML grounding.

Skip if: You have never met neural networks, or you want the underlying theory.

That hands-on approach is the appeal, but it rests on an assumption — you already understand the basics of neural networks. Meet that bar and it's an approachable, productive start; miss it and you'll be copying code you can't yet reason about, so learn the fundamentals first. It opens DeepLearning.AI's four-course TensorFlow Developer Professional Certificate on Coursera (free to audit; certificate on a subscription or Coursera Plus, financial aid available).

One point of confusion worth clearing up: this is separate from Google's own TensorFlow Developer Certificate exam, which Google retired in 2024 — this Coursera programme is still active (as of 2026).

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About this course

Introduction to TensorFlow for AI, ML and Deep Learning is the practical counterpart to theory-first courses: instead of deriving the maths, you start building working models in TensorFlow almost immediately. Laurence Moroney walks through writing your first neural networks, training an image classifier, and using convolutions to improve computer-vision results.

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Instructor

LM
Laurence Moroney
Coursera instructor

Laurence Moroney leads AI Advocacy at Google and is the author of several TensorFlow books. He's known for a code-first, plain-spoken teaching style that gets beginners building real models quickly rather than getting stuck in theory.

Frequently asked questions

No, and note an important point: it is a learning course, not the certification, and Google actually retired the official TensorFlow Developer Certificate exam in mid-2024. So the course teaches you to build models with TensorFlow, which remains genuinely useful, but there is no longer an official certificate to earn from it. Take it for the skills, not for a credential that no longer exists.

It helps but is not essential. This is a hands-on, code-first introduction to using TensorFlow, so it teaches you to build neural networks practically. If you understand the theory of how neural networks work (from a course like Andrew Ng's Deep Learning specialization), the code makes more sense; if not, you can still follow along and pick up intuition, then deepen the theory separately.

Yes. It involves writing TensorFlow code in Python, so you need to be comfortable with the language before starting. It is not a Python primer. You do not need prior TensorFlow experience, which it introduces, but arriving without working Python means struggling with the code rather than focusing on building and training models, which is the point of the course.

Yes — it is the first course in DeepLearning.AI's TensorFlow Developer Professional Certificate specialization, which teaches practical deep learning with TensorFlow across image, text, and sequence data. You can take this course alone for the introduction, but it is designed as the opening of that sequence. Bear in mind the specialization originally aimed at the now-retired official exam, so treat it as skills-focused.

Yes, you can audit it on Coursera and watch the lectures at no cost. The graded programming assignments and the certificate require a subscription, with financial aid available if you apply. Since the value is in actually building models in TensorFlow, the paid track is worth considering if you want the hands-on practice rather than only the lectures.
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