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AI+ Architect™

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Certificate Code: AT-320

Passing Score: 70% (35/50)

Exam Info: 50 MCQs, 90 Minutes

Tagline: Visualize Tomorrow: Neural Networks in Vision

Course Overview:

  • 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

Prerequisites:

  • 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.

Tools Used:

  • AutoGluon AutoGluon
  • ChatGPT ChatGPT
  • SonarCube SonarCube
  • Vertex AI Vertex AI

Modules:

  • 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

What You’ll Learn:

  • End-to-End AI Solution Development — Learners will be able to develop end-to-end AI solutions, encompassing the entire workflow from data preprocessing and model building to deployment and monitoring. This includes integrating AI models into larger systems and applications, ensuring they work seamlessly within existing infrastructures.
  • Neural Network Implementation — Learners will gain hands-on experience in implementing various neural network architectures from scratch using programming frameworks like TensorFlow or PyTorch. This includes creating, training, and debugging models for different applications.
  • AI Research and Innovation — Learners will be equipped with the ability to conduct AI research, enabling them to stay at the forefront of AI developments. This includes identifying research gaps, proposing novel solutions, and critically evaluating current AI methodologies to drive innovation in the field.
  • Generative AI and Research-Based AI Design — Learners will explore advanced concepts in generative AI models and engage in research-based AI design. This includes developing innovative AI solutions and understanding the latest advancements in AI research, preparing them for cutting-edge applications and further research opportunities.

Career Opportunities:

  • AI Architect — Specializes in designing AI models, neural networks, and intelligent systems for diverse applications, including NLP and computer vision.
  • AI Solutions Architect — Leads the integration of AI into complex systems, ensuring the deployment of scalable and efficient AI solutions across various platforms.
  • Cloud AI Architect — Designs and implements AI-powered cloud infrastructures, focusing on the seamless integration of AI models.
  • AI Research Scientist — Engages in the development of new AI models and architectures, conducting cutting-edge research.
  • AI System Integrator — Focuses on the implementation and integration of AI components into existing systems, ensuring that AI-driven solutions.

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%

Self-Study Materials:

  • Videos Videos: Engaging visual content to enhance understanding and learning experience.
  • Podcasts Podcasts: Insightful audio sessions featuring expert discussions and real-world cases.
  • Audiobooks Audiobooks: Listen and learn anytime with convenient audio-based knowledge sharing.
  • E-Books E-Books: Comprehensive digital guides offering in-depth knowledge and learning support.
  • Module Wise Quizzes Module Wise Quizzes: Interactive assessments to reinforce learning and test conceptual clarity.
  • Additional Resources Additional Resources: Supplementary references and list of tools to deepen knowledge and practical application.
  • Labs Labs: Interactive lab sessions to apply concepts and strengthen technical skills.

Frequently Asked Questions:

  • Q: What is the duration of the AI+ Architect certification course?
    A: The certification lasts 40 hours, typically completed over 5 days, providing an intensive learning experience.
  • Q: What will I learn in the AI+ Architect certification?
    A: You will learn advanced neural network techniques, model optimization, NLP and computer vision architectures, AI deployment infrastructure, and ethical AI design.
  • Q: Who should enroll in this course?
    A: This course is ideal for AI architects, engineers, software developers, and professionals seeking to master AI architectures and neural networks.
  • Q: Do I need prior experience to enroll in the AI+ Architect course?
    A: A foundational understanding of AI and neural networks is recommended but not required, as the course starts with core concepts.
  • Q: What is the outcome after completing the AI+ Architect certification?
    A: Participants will be equipped with both theoretical and practical knowledge to design, optimize, and implement AI architectures.

Certificate Features:

  • Feature Icon High-Quality Video, E-book & Audiobook
  • Feature Icon Modules Quizzes
  • Feature Icon AI Mentor
  • Feature Icon Access for Tablet & Phone
  • Feature Icon Online Proctored Exam with One Free Retake
  • Feature Icon LABs Practices