AI+ Developer™

Get hands-on with the tools and technologies that power the AI ecosystem.

AI+ Developer™ certification program offers a tailored journey in key AI domains for developers. Master Python, advanced concepts, math, stats, optimization, and deep learning. The curriculum covers data processing, exploratory analysis, and allows specialization in NLP, computer vision, or reinforcement learning. The program includes time series analysis, model explainability, and deployment intricacies. Upon completion, you'll receive a certification, showcasing your AI proficiency for real-world challenges.

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  • Basic Math: Familiarity with high school-level algebra and basic statistics is desirable.
  • Computer Science Fundamentals: Understanding the basic programming concepts (variables, functions, and loops) and data structures (lists and dictionaries).
  • Fundamental knowledge of programming skills.

Exam Details



50 MCQs 90 Minutes

Passing Score70%

Certification Modules

  1. 1.1 Introduction to AI
  2. 1.2 Types of Artificial Intelligence
  3. 1.3 Branches of Artificial Intelligence
  4. 1.4 Applications and Business Use Cases
  1. 2.1 Linear Algebra
  2. 2.2 Calculus
  3. 2.3 Probability and Statistics
  4. 2.4 Discrete Mathematics
  1. 3.1 Python Fundamentals
  2. 3.2 Python Libraries
  1. 4.1 Introduction to Machine Learning
  2. 4.2 Supervised Machine Learning Algorithms
  3. 4.3 Unsupervised Machine Learning Algorithms
  4. 4.4 Model Evaluation and Selection
  1. 5.1 Neural Networks
  2. 5.2 Convolutional Neural Networks (CNNs)
  3. 5.3 Recurrent Neural Networks (RNNs)
  1. 6.1 Image Processing Basics
  2. 6.2 Object Detection
  3. 6.3 Image Segmentation
  4. 6.4 Generative Adversarial Networks (GANs)
  1. 7.1 Text Preprocessing and Representation
  2. 7.2 Text Classification
  3. 7.3 Named Entity Recognition (NER)
  4. 7.4 Question Answering (QA)
  1. 8.1 Introduction to Reinforcement Learning
  2. 8.2 Q-Learning and Deep Q-Networks (DQNs)
  3. 8.3 Policy Gradient Methods
  1. 9.1 Cloud Computing for AI
  2. 9.2 Cloud-Based Machine Learning Services
  1. 10.1 Understanding LLMs
  2. 10.2 Text Generation and Translation
  3. 10.3 Question Answering and Knowledge Extraction
  1. 11.1 Neuro-Symbolic AI
  2. 11.2 Explainable AI (XAI)
  3. 11.3 Federated Learning
  4. 11.4 Meta-Learning and Few-Shot Learning
  1. 12.1 Communicating AI Projects
  2. 12.2 Documenting AI Systems
  3. 12.3 Ethical Considerations

What Will You Learn?

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Python Programming Proficiency

Students will gain a solid foundation in Python programming. Implementing AI algorithms, processing data, and constructing AI applications require this competence

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Deep Learning Techniques

Learners will master machine learning and deep learning techniques and methods to classification, regression, image recognition, and natural language processing challenges.

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Cloud Computing in AI Development

Students will get hands-on experience in cloud-based AI application development and learn how to use AWS, Azure, and Google Cloud for scalable AI systems.

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Project Management in AI

Participations will master the skills necessary to manage AI projects effectively, from initiation to completion, including planning, resource allocation, risk management, and stakeholder communication.

Industry Opportunities after Course Completion


Upon completion, you'll receive an AI+ Developer™ certification, showcasing your proficiency in AI. You'll have the skills to tackle real-world AI challenges and implement advanced AI solutions in various domains.

While prior AI knowledge is not mandatory, a fundamental understanding of Python programming and basic math and statistics will help you grasp the advanced concepts covered in this course.

Yes, the course includes various hands-on projects and practical exercises to help you apply theoretical concepts to real-world scenarios, reinforcing your learning through practical experience.

You cannot choose a specialization in this course. However, you will be trained in areas such as Natural Language Processing (NLP), computer vision, and reinforcement learning.

Your progress will be evaluated through a combination of quizzes, hands-on exercises, and a final assessment. These evaluations are designed to test your understanding and application of the material.