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DeepLearning.AI · AI & Machine Learning · Associate

Deep Learning Specialization / AI credentials overview exam guide

Exam No proctored exam (course certificates; TensorFlow Developer Certificate exam closed May 2024)

Google's TensorFlow Developer Certificate exam closed in 2024 (last test date May 31, 2024), so learners now use DeepLearning.AI's course credentials, led by the Deep Learning Specialization and the TensorFlow Developer Professional Certificate on Coursera. These are course-completion certificates, not proctored exams, and are valued as structured proof of neural-network fundamentals before pursuing a proctored ML certification.

QuestionsSee official page
Time limitSee official page
Passing scorePass all graded quizzes and assignments
Exam feeSee official page
Valid forSee official page
DeliveryOnline, self-paced on Coursera and DeepLearning.AI

Format: Graded quizzes and programming assignments inside each course. Fee note: Paid through a Coursera subscription or per-course purchase; financial aid available on Coursera.

Who it's for

Developers, analysts and students who want a structured path into neural networks, CNNs, sequence models and practical TensorFlow before attempting a proctored cloud ML certification.

What the exam covers

Neural Networks and Deep Learning—
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization—
Structuring Machine Learning Projects—
Convolutional Neural Networks—
Sequence Models—

A dash means the vendor does not publish a weight for that domain.

Eligibility & prerequisites

Required: None formally; Python and basic linear algebra and calculus are assumed.

Recommended: Intermediate Python, familiarity with NumPy, and high-school level math (matrix multiplication, derivatives).

Keeping it active

Course certificates do not expire and have no renewal requirement.

Skills & careers

Skills it validates

  • Implementing forward and back propagation from scratch
  • Regularization, dropout, batch normalization and optimizers (Adam, RMSprop)
  • Diagnosing bias and variance and error analysis
  • Building CNNs for image classification and detection
  • Building RNNs, LSTMs and attention/transformer models for sequences
  • Training models in TensorFlow/Keras

Job roles

Machine Learning EngineerDeep Learning EngineerData ScientistAI EngineerComputer Vision EngineerNLP Engineer

Where it fits in your path

Planning a degree too? See how this cert earns college credit in the College Credit Navigator.

Build my study plan

  1. Weeks 1-3: Course 1 (Neural Networks and Deep Learning); implement every notebook yourself rather than filling blanks only.
  2. Weeks 4-6: Course 2 (hyperparameter tuning, regularization, optimization) and Course 3 (structuring ML projects).
  3. Weeks 7-10: Course 4 (CNNs); rebuild one assignment in plain Keras on your own dataset.
  4. Weeks 11-13: Course 5 (sequence models and transformers); fine-tune a small text model.
  5. Weeks 14-16: Publish two end-to-end projects on GitHub, then book a proctored cloud ML exam.

Make it yours: pick your exam date and weekly hours. We'll split your time by domain weight and give you a printable plan.

Exam-day tips

  • There is no final exam; employers value the projects you build, so extend assignments into portfolio pieces.
  • Do not search for the old TensorFlow Developer Certificate exam; it is no longer offered and old dumps are worthless.
  • Keep the math notebooks from Course 1; they are the best review for ML interview questions.
  • Pair the specialization with a proctored credential (Google Professional ML Engineer or AWS ML Engineer Associate) for hiring filters.
  • Audit options vary by course; confirm whether graded assignments require the paid track before starting.

Key terms for this exam

All 40 terms Study with thinQcards™

Frequently asked questions

Is the TensorFlow Developer Certificate still available?

No. Google closed the program; the last day to buy the exam was April 30, 2024 and the last day to take it was May 31, 2024. Existing certificates remain on record but cannot be renewed.

What replaced the TensorFlow Developer Certificate?

There is no direct replacement. Most learners take the DeepLearning.AI Deep Learning Specialization or TensorFlow Developer Professional Certificate on Coursera, then a proctored cloud ML certification.

How long does the Deep Learning Specialization take?

About three to four months at roughly five to eight hours per week across five courses.

Is a DeepLearning.AI certificate worth it?

It is respected for teaching fundamentals well, but it is a course-completion credential; pair it with projects and a proctored certification for job applications.

Deep Learning Specialization vs Google Professional ML Engineer?

The specialization teaches model theory and implementation; the Google exam tests designing, deploying and operating ML systems on Google Cloud. Most people do the specialization first.

Official page: www.deeplearning.ai. Reviewed November 2026. Vendors change fees and objectives, so confirm with the vendor before you book. Spot something out of date? Tell us.