AI+ Architect™

Course code: AT320

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

Price of the certification exam is included in the price of the course.

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Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 445 EUR

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Course description

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.

  • AutoGluon
  • ChatGPT
  • SonarCube
  • Vertex AI

Target group

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.

Course structure

Certification Overview

  1. Course Introduction Preview

Module 1: Fundamentals of Neural Networks

  1. 1.1 Introduction to Neural Networks
  2. 1.2 Neural Network Architecture
  3. 1.3 Hands-on: Implement a Basic Neural Network

Module 2: Neural Network Optimization

  1. 2.1 Hyperparameter Tuning
  2. 2.2 Optimization Algorithms
  3. 2.3 Regularization Techniques
  4. 2.4 Hands-on: Hyperparameter Tuning and Optimization

Module 3: Neural Network Architectures for NLP

  1. 3.1 Key NLP Concepts
  2. 3.2 NLP-Specific Architectures
  3. 3.3 Hands-on: Implementing an NLP Model

Module 4: Neural Network Architectures for Computer Vision

  1. 4.1 Key Computer Vision Concepts
  2. 4.2 Computer Vision-Specific Architectures
  3. 4.3 Hands-on: Building a Computer Vision Model

Module 5: Model Evaluation and Performance Metrics

  1. 5.1 Model Evaluation Techniques
  2. 5.2 Improving Model Performance
  3. 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: AI Infrastructure and Deployment

  1. 6.1 Infrastructure for AI Development
  2. 6.2 Deployment Strategies
  3. 6.3 Hands-on: Deploying an AI Model

Module 7: AI Ethics and Responsible AI Design

  1. 7.1 Ethical Considerations in AI
  2. 7.2 Best Practices for Responsible AI Design
  3. 7.3 Hands-on: Analyzing Ethical Considerations in AI

Module 8: Generative AI Models

  1. 8.1 Overview of Generative AI Models
  2. 8.2 Generative AI Applications in Various Domains
  3. 8.3 Hands-on: Exploring Generative AI Models

Module 9: Research-Based AI Design

  1. 9.1 AI Research Techniques
  2. 9.2 Cutting-Edge AI Design
  3. 9.3 Hands-on: Analyzing AI Research Papers

Module 10: Capstone Project and Course Review

  1. 10.1 Capstone Project Presentation
  2. 10.2 Course Review and Future Directions
  3. 10.3 Hands-on: Capstone Project Development

Optional Module: AI Agents for Architect

  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

Prerequisites

key concepts in both artificial intelligence, Fundamental understanding of computer science, Familiarity with cloud computing platforms like AWS, Azure, or GCP

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Certification

50 questions, 70% passing, 90 minutes, online proctored exam

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