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

Included: Instructor-led OR Self-paced course + Official exam + Digital badge

Delivery: Online labs, projects, case studies

Outcome: Industry-recognized credential + hands-on experience

The following tools will be explored in this course:

• AutoGluon
• ChatGPT
• SonarCube
• Vertex AI

What’s Included (One-Year Subscription + All Updates):

• High-Quality Videos, E-book (PDF & Audio), and Podcasts
• AI Mentor for Personalized Guidance
• Quizzes, Assessments, and Course Resources
• Online Proctored Exam with One Free Retake
• Comprehensive Exam Study Guide
• Access for Tablet & Phone

• Advanced Neural Network Design
• AI Model Evaluation & Performance Metrics
• Generative AI for Architecture
• AI Deployment & Infrastructure
• Machine Learning Optimization Shape

• Architecture Professionals: Enhance your architectural design skills by integrating AI to create scalable, efficient, and intelligent systems for modern solutions.
• Systems Architects & Engineers: Learn to leverage AI to design and build sophisticated, scalable infrastructures while automating key processes.
• IT Infrastructure Managers: Use AI to optimize architecture planning, streamline infrastructure deployment, and ensure seamless system integration.
• Business Leaders: Drive transformation within your organization by adopting AI-driven architectural solutions to enhance scalability, reduce costs.
• Students & New Graduates: Gain a competitive edge in the tech industry by mastering AI architectural techniques and tools.

• A foundational knowledge on neural networks, including their optimization and architecture for applications.
• Ability to evaluate models using various performance metrics to ensure accuracy and reliability.
• Willingness to know about AI infrastructure and deployment processes to implement and maintain AI systems effectively.

Certification Overview
Course Introduction

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

• AI+ Cloud™

Exam Details

• Duration: 90 minutes
• Passing Score: 70% (35/50)
• Format: 50 multiple-choice/multiple-response questions
• Delivery Method: Online via proctored exam platform (flexible scheduling)

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%