AI+ Architect™

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$174.50

Intermediate

AI+ Architect.

40h

1 Year.

Training 5 or more people?

Visualize Tomorrow: Neural Networks in Vision.

Deep AI Expertise: Covers neural networks, NLP, and computer vision frameworks
Enterprise AI: Learn to design scalable AI systems for real-world impact
Capstone Integration: Build, test, and deploy advanced AI architectures
Industry Preparedness: Equips you for roles in high-demand AI design domains
Leverage AI for Smarter Architecture Decisions: Learn how to use AI tools to optimize architectural design, improve scalability.
Enhance AI Integration in Architectural Projects: Use AI to integrate innovative solutions into your architectural designs, automating workflows.
Stay Ahead in AI-Powered Architecture Innovation: As AI adoption in architecture accelerates, professionals with advanced AI knowledge.
Boost Strategic Decision-Making with AI Insights: Master AI models to analyze architectural data, predict trends, and drive data-driven decisions.
Advance Your Career in AI Architecture: As AI revolutionizes architecture, this certification equips you with the skills to lead AI initiatives.

Course Curriculum

Module 1: Fundamentals of Neural Networks
1.1 Introduction to Neural Networks 1.2 Neural Network Architecture 1.3 Hands-on: Implement a Basic Neural Network

Module 2: Neural Network Optimization
2.1 Hyperparameter Tuning 2.2 Optimization Algorithms 2.3 Regularization Techniques 2.4 Hands-on: Hyperparameter Tuning and Optimization

Module 3: Neural Network Architectures for NLP
3.1 Key NLP Concepts 3.2 NLP-Specific Architectures 3.3 Hands-on: Implementing an NLP Model

Module 4: Neural Network Architectures for Computer Vision
4.1 Key Computer Vision Concepts 4.2 Computer Vision-Specific Architectures 4.3 Hands-on: Building a Computer Vision Model

Module 5: Model Evaluation and Performance Metrics
5.1 Model Evaluation Techniques 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: AI Infrastructure and Deployment
6.1 Infrastructure for AI Development 6.2 Deployment Strategies 6.3 Hands-on: Deploying an AI Model

Module 7: AI Ethics and Responsible AI Design
7.1 Ethical Considerations in AI 7.2 Best Practices for Responsible AI Design 7.3 Hands-on: Analyzing Ethical Considerations in AI

Module 8: Generative AI Models
8.1 Overview of Generative AI Models 8.2 Generative AI Applications in Various Domains 8.3 Hands-on: Exploring Generative AI Models

Module 9: Research-Based AI Design
9.1 AI Research Techniques 9.2 Cutting-Edge AI Design 9.3 Hands-on: Analyzing AI Research Papers

Module 10: Capstone Project and Course Review
10.1 Capstone Project Presentation 10.2 Course Review and Future Directions 10.3 Hands-on: Capstone Project Development

Optional Module: AI Agents for Architect
1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents

The AI+ Architect certification offers comprehensive training in advanced neural network techniques and architectures.
It covers the fundamentals of neural networks, optimization strategies, and specialized architectures for natural language processing (NLP) and computer vision.
Participants will learn about model evaluation, performance metrics, and the infrastructure required for AI deployment.
The course emphasizes ethical considerations and responsible AI design, alongside exploring cutting-edge generative AI models and research-based AI design methodologies.
A capstone project and course review consolidate learning, ensuring participants can apply their skills effectively in real-world scenarios.
This certification equips learners with the knowledge and practical experience to excel in AI architecture and development.

Job Roles & Industry Outlook

Al Solutions Architect

Leads the integration of Al into complex systems, ensuring the deployment of scalable and efficient Al solutions across various platforms.

Cloud Al Architect

Designs and implements Al- powered cloud infrastructures, focusing on the seamless integration of Al models.

Al Research Scientist

Engages in the development of new Al models and architectures, conducting cutting-edge research.

Al System Integrator

Focuses on the implementation and integration of Al components into existing systems, ensuring that Al- driven solutions.

Al Architect

Specializes in designing Al models, neural networks, and intelligent systems for diverse applications, including NLP and computer vision.

Exam Information

Duration

90 minutes

Passing Score

70% (35/50)

Format

50 multiple-choice/multiple-response questions

Delivery Method

Online via proctored exam platform (flexible scheduling)

Fees include

One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Fundamentals of Neural Networks – 10%

Neural Network Optimization – 10%

Neural Network Architectures for NLP – 10%

Neural Network Architectures for Computer Vision – 10%

Model Evaluation and Performance Metrics – 10%

AI Infrastructure and Deployment – 10%

AI Ethics and Responsible AI Design – 10%

Generative AI Models – 10%

Research-Based AI Design – 10%

Capstone Project and Course Review – 10%

Explore our Schedules

Aug 23 - Aug 27

GMT 09:00 AM - 05:00 PM

In-person

Riyadh

880.5$

5 Days

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Aug 9 - Aug 13

GMT 09:00 AM - 05:00 PM

In-person

Doha

880.5$

5 Days

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Oct 4 - Oct 8

GMT 09:00 AM - 05:00 PM

In-person

Dubai

880.5$

5 Days

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Aug 23 - Aug 27

GMT 09:00 AM - 05:00 PM

In-person

Kuwait

880.5$

5 Days

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Aug 9 - Aug 13

GMT 09:00 AM - 05:00 PM

In-person

Muscat

880.5$

5 Days

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Jun 15 - Jun 28

GMT 06:00 PM - 10:00 PM

LVT

Zoom

643.2$

10 Days

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Oct 27 - Nov 9

GMT 06:00 PM - 10:00 PM

LVT

Zoom

643.2$

10 Days

Enroll Now

Certificate of Completion

AI+ Architect.

$174.50

Intermediate

AI+ Architect.

40h

1 Year.

Training 5 or more people?

AI+ Architect™

Visualize Tomorrow: Neural Networks in Vision.

Course Curriculum

Module 1: Fundamentals of Neural Networks 1.1 Introduction to Neural Networks 1.2 Neural Network Architecture 1.3 Hands-on: Implement a Basic Neural Network

Module 2: Neural Network Optimization 2.1 Hyperparameter Tuning 2.2 Optimization Algorithms 2.3 Regularization Techniques 2.4 Hands-on: Hyperparameter Tuning and Optimization

Module 3: Neural Network Architectures for NLP 3.1 Key NLP Concepts 3.2 NLP-Specific Architectures 3.3 Hands-on: Implementing an NLP Model

Module 4: Neural Network Architectures for Computer Vision 4.1 Key Computer Vision Concepts 4.2 Computer Vision-Specific Architectures 4.3 Hands-on: Building a Computer Vision Model

Module 5: Model Evaluation and Performance Metrics 5.1 Model Evaluation Techniques 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: AI Infrastructure and Deployment 6.1 Infrastructure for AI Development 6.2 Deployment Strategies 6.3 Hands-on: Deploying an AI Model

Module 7: AI Ethics and Responsible AI Design 7.1 Ethical Considerations in AI 7.2 Best Practices for Responsible AI Design 7.3 Hands-on: Analyzing Ethical Considerations in AI

Module 8: Generative AI Models 8.1 Overview of Generative AI Models 8.2 Generative AI Applications in Various Domains 8.3 Hands-on: Exploring Generative AI Models

Module 9: Research-Based AI Design 9.1 AI Research Techniques 9.2 Cutting-Edge AI Design 9.3 Hands-on: Analyzing AI Research Papers

Module 10: Capstone Project and Course Review 10.1 Capstone Project Presentation 10.2 Course Review and Future Directions 10.3 Hands-on: Capstone Project Development

Optional Module: AI Agents for Architect 1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents

Learning Objectives

Resource Center

Target Audience

Job Roles & Industry Outlook

Exam Information

Exam Blueprint:

Explore our Schedules

Certificate of Completion

AI+ Architect.

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Module 1: Fundamentals of Neural Networks
1.1 Introduction to Neural Networks 1.2 Neural Network Architecture 1.3 Hands-on: Implement a Basic Neural Network

Module 2: Neural Network Optimization
2.1 Hyperparameter Tuning 2.2 Optimization Algorithms 2.3 Regularization Techniques 2.4 Hands-on: Hyperparameter Tuning and Optimization

Module 3: Neural Network Architectures for NLP
3.1 Key NLP Concepts 3.2 NLP-Specific Architectures 3.3 Hands-on: Implementing an NLP Model

Module 4: Neural Network Architectures for Computer Vision
4.1 Key Computer Vision Concepts 4.2 Computer Vision-Specific Architectures 4.3 Hands-on: Building a Computer Vision Model

Module 5: Model Evaluation and Performance Metrics
5.1 Model Evaluation Techniques 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: AI Infrastructure and Deployment
6.1 Infrastructure for AI Development 6.2 Deployment Strategies 6.3 Hands-on: Deploying an AI Model

Module 7: AI Ethics and Responsible AI Design
7.1 Ethical Considerations in AI 7.2 Best Practices for Responsible AI Design 7.3 Hands-on: Analyzing Ethical Considerations in AI

Module 8: Generative AI Models
8.1 Overview of Generative AI Models 8.2 Generative AI Applications in Various Domains 8.3 Hands-on: Exploring Generative AI Models

Module 9: Research-Based AI Design
9.1 AI Research Techniques 9.2 Cutting-Edge AI Design 9.3 Hands-on: Analyzing AI Research Papers

Module 10: Capstone Project and Course Review
10.1 Capstone Project Presentation 10.2 Course Review and Future Directions 10.3 Hands-on: Capstone Project Development

Optional Module: AI Agents for Architect
1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents