Artificial Intelligence Expert Certificate (CAIEC)

$150.00 USD

Available Now!

Become an Artificial Intelligence Expert Certified.

Artificial Intelligence Expert Certificate is available to take or re-take online.

Exam Code: CAIEC

Exam format

  • Multiple choice
  • 40 questions
  • Passing score: 80%
  • 60 minutes duration
  • Closed book
  • Unproctored exam delivered through

You will have two attempts within 180 calendar days after you receive your initial welcome email to pass the test at no cost.


Please read the following carefully before proceeding:

  • You may take the exam immediately or within 6 months from payment date.
  • Once the exam is purchased and taken you won’t be eligible for a refund.

Retake Policy

Each exam purchase includes 1 free retake.

Become an Artificial Intelligence Expert Certified.


AI is transforming many industries and changing the world. From effective web searches and speech recognition to self-driving cars, machine translation, natural language processing, and more.

Expand your knowledge in AI, Machine Learning and Deep Learning. Master the bases of neural networks architecture and the strengths of the many libraries and tools you will have at your disposal such as Python and TensorFlow among many others. 

Learn about the most effective machine learning techniques, and practice implementing them and getting them to work for yourself. 

Understand the capabilities, challenges, and consequences of deep learning and prepare to participate in the development of leading-edge AI technology.

Take the definitive step in the world of AI, gain the knowledge and skills to level up your career.

  • Engineers
  • Analysts
  • Marketing managers
  • Data Analysts
  • Data Scientists
  • Data Steward
  • Anyone interested in Data Mining and Machine Learning techniques
  • Understand the fundamental keys of the deep learning approach
  • Master the theoretical and practical bases of architecture and convergence of neural networks
  • Depicting different existing fundamental architectures and mastering fundamental implementations
  • Master the methodologies for setting up neural networks, the strengths and limitations of existing tools and libraries (pandas, numpy, scikit-learn)

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