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Certification

Certification in Building and Deploying AI & Machine Learning Solutions

Get certified in Artificial Intelligence and Machine Learning and demonstrate your ability to build intelligent solutions, apply key algorithms, and deliver real-world projects that solve complex problems across industries.

Exam of 10 to 20 questionsCertificate for LinkedInBeginner · Intermediate
IT and developmentScience and researchEducation
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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. 01What is AI32:41
  2. 02AI with Python99:06
  3. 03ML basics and linear regression48:08
  4. 04Logistic regression47:09
  5. 05Linear vs logistic and decision trees49:05
  6. 06Random forest25:15
  7. 07KNN32:23
  8. 08Naive Bayes and SVM47:12
  9. 09K-means clustering23:20
  10. 10Hierarchical clustering, Apriori and deep learning45:55
  11. 11Artificial neural networks32:21
  12. 12CNNs and RNNs49:27
  13. 13LSTM53:57
  14. 14Transformers, GANs and the future36:59
  15. 15AI interview questions85:39
About the course

The "Artificial Intelligence and Machine Learning: Complete Course with Projects (Video Course)" certification offers a comprehensive pathway to mastering the fundamentals and practical applications of AI and machine learning. Gain skills that enhance productivity, provide a competitive advantage, and prepare you for a future-proof career in this dynamic field. Enroll today to unlock your potential and confidently address real-world challenges using cutting-edge technology.

This certification covers the following topics:

  • Artificial Intelligence: Concepts, Stages, and Types
  • Core Principles of Machine Learning
  • Machine Learning Processes and Key Features
  • Types of Machine Learning Algorithms
  • Deep Learning and Neural Networks
  • Natural Language Processing (NLP) and Applications
  • Generative Adversarial Networks (GANs)
  • Decision Trees and Random Forests
  • K-Nearest Neighbors (KNN) and Naive Bayes
  • Support Vector Machines (SVMs) and Clustering Techniques
  • Market Basket Analysis and Apriori Algorithm
  • Hyperparameter Optimization and Overfitting Solutions
  • AI in Real-World Applications and Problem Solving