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intermediateCertificate$49/mo

TensorFlow Developer Professional Certificate

by Laurence Moroney · Coursera

4.7
(35,000 reviews)
500K+ enrolled4 monthsUpdated 2024-08
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Our Verdict

Worth it — with caveats

The DeepLearning.AI TensorFlow Developer Professional Certificate (offered by DeepLearning.AI on Coursera, taught by Google AI advocate Laurence Moroney) is a hands-on, code-first introduction to building neural networks with TensorFlow and Keras, and it earns a strong 4.7/5 from 25,393 Coursera reviews. Across four short courses it moves you from a 'Hello World' dense network to CNNs for computer vision, NLP with embeddings and LSTMs, and time-series forecasting, reinforced by 16 Python programming assignments run in Google Colab. It is a genuinely good practitioner on-ramp, but independent reviews consistently note it is light on math and theory, leans heavily on the high-level Keras API rather than lower-level TensorFlow, and has quizzes that are too easy. One important caveat for 2026: it was widely marketed as prep for Google's TensorFlow Developer Certificate exam, but Google retired that exam (last exam date May 31, 2024), so the credential it once led toward no longer exists.

Excellent, well-paced practical introduction to coding deep learning with TensorFlow/Keras for people who already know Python and want to ship models fast. Take it for the hands-on skills, not for theoretical depth or for the (now-discontinued) Google TensorFlow exam. Learners wanting mathematical rigor, or pure beginners with no Python/ML background, should pair it with or substitute a more theory-focused course.

Best for: Developers and data analysts who are already comfortable with Python and want a fast, project-based path to building real neural networks (computer vision, NLP, and time-series forecasting) in TensorFlow/Keras. It is also a strong practical complement to a theory-first course like Andrew Ng's Deep Learning Specialization, and a good fit for people who learn best by writing code in Colab rather than reading proofs.

Skip if: Complete beginners with no Python or basic ML/linear-algebra background (independent reviewers say the pace is too fast for them), researchers or engineers wanting deep mathematical rigor and from-scratch implementations, and anyone enrolling specifically to earn Google's TensorFlow Developer Certificate, which was discontinued in 2024.

About This Course

Four-course certificate covering TensorFlow for building neural networks, CNNs, NLP models, and time series forecasting.

What You'll Learn

Build and train basic dense neural networks in TensorFlow/Keras, including a first computer-vision model
Apply convolutions and pooling to build CNNs, work with real-world image data, use image augmentation and transfer learning, and reduce overfitting
Process text with tokenization and word embeddings, and build NLP models using RNNs, GRUs, and LSTMs
Forecast univariate time series using DNNs, RNNs/LSTMs, and convolutional approaches
Practice the full applied workflow (data prep, model training, and evaluation) across 16 Python programming assignments in Google Colab

Curriculum

Course 1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

TensorFlow/Keras basics: build your first dense neural network, then a computer-vision classifier, and use convolutions to improve it.

Course 2 - Convolutional Neural Networks in TensorFlow

Work with larger real-world image datasets; apply image augmentation, transfer learning, and dropout to improve CNNs and combat overfitting.

Course 3 - Natural Language Processing in TensorFlow

Tokenize and represent text with embeddings; build sentiment and text models with RNNs, GRUs, and LSTMs, including text generation.

Course 4 - Sequences, Time Series and Prediction

Forecast time-series data using statistical baselines plus DNNs, RNNs/LSTMs, and 1D convolutional models.

Prerequisites

  • Intermediate Python programming skills
  • Basic linear algebra and introductory machine learning concepts (helpful, not strictly enforced)
  • Coursera lists the certificate as 'Intermediate level' with recommended prior experience

Instructor

Laurence Moroney

Instructor · Coursera

Pros & Cons

Pros

  • Highly practical and code-first: 16 Python programming assignments in Google Colab take you from a simple model to CNNs, NLP, and forecasting
  • Laurence Moroney is widely praised for explaining concepts clearly and accessibly, with bonus interview segments featuring Andrew Ng
  • Logical four-course progression that builds skills incrementally and covers a broad range of applied DL domains
  • All four courses can be audited free on Coursera (assignments/certificate require the paid subscription), lowering the barrier to try it
  • Strong, durable reputation: 4.7/5 from 25,393 Coursera reviews and consistently positive Reddit/Class Central sentiment as a TensorFlow on-ramp

Cons

  • Light on math and theory; relies heavily on the high-level Keras API rather than lower-level TensorFlow, so you learn to use models more than to understand them deeply
  • Quizzes and some assignments are widely described as too easy / not challenging enough
  • Pace is too fast for true beginners who lack prior Python or deep-learning exposure
  • Marketed as prep for the Google TensorFlow Developer Certificate exam, which Google discontinued (last exam May 31, 2024), so that specific payoff no longer exists

Alternatives To Consider

Deep Learning Specialization

Coursera

View course

Practical Deep Learning for Coders

fast.ai

View course

PyTorch for Deep Learning & Machine Learning

Udemy

View course

Frequently Asked Questions

Is TensorFlow Developer Professional Certificate free?

TensorFlow Developer Professional Certificate is $49/mo. Free to audit all four courses (video lectures). Graded assignments and the shareable certificate require Coursera's paid subscription, listed around $49/month, so total cost depends on how quickly you finish; a 7-day free trial is typically available. The catalog price of $49/mo is accurate as a subscription rate.

Who is TensorFlow Developer Professional Certificate for?

Developers and data analysts who are already comfortable with Python and want a fast, project-based path to building real neural networks (computer vision, NLP, and time-series forecasting) in TensorFlow/Keras. It is also a strong practical complement to a theory-first course like Andrew Ng's Deep Learning Specialization, and a good fit for people who learn best by writing code in Colab rather than reading proofs.

What will you learn in TensorFlow Developer Professional Certificate?

Build and train basic dense neural networks in TensorFlow/Keras, including a first computer-vision model; Apply convolutions and pooling to build CNNs, work with real-world image data, use image augmentation and transfer learning, and reduce overfitting; Process text with tokenization and word embeddings, and build NLP models using RNNs, GRUs, and LSTMs; Forecast univariate time series using DNNs, RNNs/LSTMs, and convolutional approaches.

What are the prerequisites for TensorFlow Developer Professional Certificate?

Intermediate Python programming skills; Basic linear algebra and introductory machine learning concepts (helpful, not strictly enforced); Coursera lists the certificate as 'Intermediate level' with recommended prior experience.

Is TensorFlow Developer Professional Certificate worth it?

Excellent, well-paced practical introduction to coding deep learning with TensorFlow/Keras for people who already know Python and want to ship models fast. Take it for the hands-on skills, not for theoretical depth or for the (now-discontinued) Google TensorFlow exam. Learners wanting mathematical rigor, or pure beginners with no Python/ML background, should pair it with or substitute a more theory-focused course.

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on Coursera's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

$49/mo
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