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Certification: Foundations of AI and Neural Networks for Practical Application

Show the world you have AI skills—this certification demonstrates your ability to apply foundational AI and neural network concepts to real-world scenarios, preparing you to confidently address emerging challenges across industries.

Exam of 10 to 20 questionsCertificate for LinkedInBeginner
IT and development
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The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

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What the exam covers

2 questions from each of the 15 chapters of the course

20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01Introduction7:12
  2. 02The playground7:22
  3. 03One neuron14:33
  4. 04Clarifications3:09
  5. 05Genetic algorithm10:48
  6. 06Two inputs19:38
  7. 07Hidden layers and misconceptions11:36
  8. 08More outputs41:41
  9. 09Traffic rules28:11
  10. 10Compass sensor11:10
  11. 11The need for shortest path1:50
  12. 12Updating the codebase20:29
  13. 13Dijkstra's algorithm26:24
  14. 14Dijkstra with AI agents19:10
  15. 15Final challenge1:04
About the course

The Certification: Foundations of AI and Neural Networks for Practical Application introduces you to the essential concepts and practical skills needed to understand and implement neural networks. Gain valuable expertise such as improved decision-making, adaptability, and a future-proof career in the growing field of artificial intelligence. Enroll today to unlock new opportunities and stay ahead in technology-driven industries.

This certification covers the following topics:

  • Fundamentals of a Simple Self-Driving Car Neural Network
  • The Role of Weights and Biases
  • Visualisation of Neural Network Decisions
  • Introduction to Hidden Layers and Multi-Layer Perceptrons
  • Problem Solving through Neural Network Design
  • Introduction to Pathfinding with Dijkstra's Algorithm
  • The Use of Genetic Algorithms for Optimisation
  • Combining Multiple Inputs and Outputs
  • Interpretability and Visualisation of Neural Networks
  • Key Challenges and Solutions in Neural Network Training
  • Ethical Considerations in Neural Network Deployment
  • Practical Applications of Neural Networks Beyond Self-Driving Cars