Development & Engineering · Self-paced
AI+ Developer™
Master Python, Machine Learning, Deep Learning, and AI Application Development.
Executive summary
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.
Built for these roles
Before you start
Basic Math: Familiarity with high school-level algebra and basic statistics.
Computer Science Fundamentals: Understanding basic programming concepts (variables, functions, loops) and data structures (lists, dictionaries).
Python Programming: Proficiency in Python is mandatory for hands-on exercises and project work.
One-time price
$280
40 hours, self-paced. Lifetime access, certificate included.
Certification exam included (limited attempts).
Secure checkout via Stripe. Instant access after payment.
Curriculum
What you'll cover.
40 hours of self-paced content. Work through it in order, on your schedule.
Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI
1.2 Types of Artificial Intelligence
1.3 Branches of Artificial Intelligence
1.4 Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
2.1 Linear Algebra
2.2 Calculus
2.3 Probability & Statistics
2.4 Discrete Mathematics
Module 3: Python for Developer
3.1 Python fundamentals
3.2 Python Libraries
Module 4: Mastering Machine Learning
4.1 Introduction to Machine Learning
4.2 Supervised Machine Learning Algorithms
4.3 Unsupervised Machine Learning Algorithms
4.4 Model Evaluation and Selection
Module 5: Deep Learning
5.1 Neural Networks
5.2 Convolutional Neural Networks (CNNs)
5.3 Recurrent Neural Networks (RNNs)
Module 6: Computer Vision
6.1 Image Processing Basics
6.2 Object Detection
6.3 Image Segmentation
6.4 Generative Adversarial Networks (GANs)
Module 7: Natural Language Processing
7.1 Text Preprocessing and Representation
7.2 Text Classification
7.3 Named Entity Recognition (NER)
7.4 Question Answering (QA)
Module 8: Reinforcement Learning
8.1 Introduction to Reinforcement Learning
8.2 Q-Learning and Deep Q-Networks (DQNs)
8.3 Policy Gradient Methods
Module 9: Cloud Computing in AI Development
9.1 Cloud Computing for AI
9.2 Cloud-Based Machine Learning Services
Module 10: Large Language Models
10.1 Understanding LLMs
10.2 Text Generation and Translation
10.3 Question Answering and Knowledge Extraction
Module 11: Cutting-Edge AI Research
11.1 Neuro-Symbolic AI
11.2 Explainable AI (XAI)
11.3 Federated Learning
11.4 Meta-Learning and Few-Shot Learning
Module 12: AI Communication and Documentation
12.1 Communicating AI Projects
12.2 Documenting AI Systems
12.3 Ethical Considerations
Ready to get certified?
Start today, learn at your own pace, and add a globally recognised credential to your name.
Trusted by governments and enterprises across the GCC.