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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.
Buy Exam Bundle Download Blueprint Find a Training Partner Download Executive SummaryPrerequisites
- 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.
Modules
12
Examination
1
50 MCQs
90 Minutes
Passing Score
70%
Certification Modules
- 1.1 Introduction to AI
- 1.2 Types of Artificial Intelligence
- 1.3 Branches of Artificial Intelligence
- 1.4 Applications and Business Use Cases
- 2.1 Linear Algebra
- 2.2 Calculus
- 2.3 Probability and Statistics
- 2.4 Discrete Mathematics
- 3.1 Python Fundamentals
- 3.2 Python Libraries
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
- 5.1 Neural Networks
- 5.2 Convolutional Neural Networks (CNNs)
- 5.3 Recurrent Neural Networks (RNNs)
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations
Certification Modules
- 1.1 Introduction to AI
- 1.2 Types of Artificial Intelligence
- 1.3 Branches of Artificial Intelligence
- 1.4 Applications and Business Use Cases
- 2.1 Linear Algebra
- 2.2 Calculus
- 2.3 Probability and Statistics
- 2.4 Discrete Mathematics
- 3.1 Python Fundamentals
- 3.2 Python Libraries
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
- 5.1 Neural Networks
- 5.2 Convolutional Neural Networks (CNNs)
- 5.3 Recurrent Neural Networks (RNNs)
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations
What Will You Learn?
Python Programming Proficiency
Students will gain a solid foundation in Python programming. Implementing AI algorithms, processing data, and constructing AI applications require this competence
Deep Learning Techniques
Learners will master machine learning and deep learning techniques and methods to classification, regression, image recognition, and natural language processing challenges.
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.
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.
Hear it from the Learners
Marc H
Happy to share I've completed the AI+ Executive Certification from AI CERTs! This program has sharpened my skills in strategic AI application + implementation, further equipping me to lead AI-driven organizational transformation.
Georgia L
As VP Operations, my recent completion of the AI+ Executive exam through AI CERTs was a pivotal step in advancing my AI skill set as we embrace an AI-driven future. This certification not only deepened my understanding of AI's broad impact across various divisions but also equipped me with the tools to make informed, strategic decisions.
Prakash G
I recently completed AI+ Executive course by AI CERTs and it was a fantastic experience! I learned everything from the basics of AI to applying AI in real business situations. It was an eye-opener for me as I could learn things about AI in detail with real examples. Thanks to AI CERTs for making complex concepts easy to understand and apply!
Kunj F
I'm thrilled to share that I have earned the AICT certification from AI CERTs! This immersive experience has not only enriched my understanding of artificial intelligence but has also fueled my passion for cutting-edge technologies shaping the future. I am excited to bring the power of AI into real-world applications and explore the depths of AI for business innovation and strategic solutions.
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