Join us on 21 Feb'26, Saturday For BIRD Career Reinvention Workshop and Accelerate Your Career Growth!
+61 439 052 199 contactus@birdmois.com
Login

    • AI+ Architect™

    • 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
    Enroll Now Enquire Now
    AI+ Architect™
    Price: USD $450.00

    At a Glance: Course + Exam Overview

    Category AI Cloud
    AI Technical
    Program Name AI+ Architect™
    Duration
    • Instructor-Led: 5 Days
    • Self-Paced: 30 hours of content
    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.
    Exam Format 50 questions, 70% passing, 90 Minutes

    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.

    Certification Modules

    Certification Overview

    1. Course IntroductionPreview

    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

    Finish the course and get certified

    Certificate 1

    Industry Opportunities

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

    Frequently Asked Questions

    The certification lasts 40 hours, typically completed over 5 days, providing an intensive learning experience.

    You will learn advanced neural network techniques, model optimization, NLP and computer vision architectures, AI deployment infrastructure, and ethical AI design.

    This course is ideal for AI architects, engineers, software developers, and professionals seeking to master AI architectures and neural networks.

    A foundational understanding of AI and neural networks is recommended but not required, as the course starts with core concepts.

    Participants will be equipped with both theoretical and practical knowledge to design, optimize, and implement AI architectures.

    Prerequisites

    `

    Exam Details

    Duration

    90 Minutes

    Passing Score

    70%

    Format

    50 multiple-choice/multiple-response questions

    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%
    Course Price: USD $450.00
    Self-Paced Online
    • ~30 hours of on-demand video lessons, e-book, podcasts, and interactive labs
    • Learn anywhere, anytime, with modular quizzes to track progress
    Purchase Self-Paced Course
    Instructor-Led (Live Virtual/Classroom)
    • 5 days of intensive training with live demos
    • Real-time Q&A, peer collaboration, and hands-on labs
    • Led by AI Certified Trainers and delivered through Authorized Training Partners