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AI+ Architect™

AT-320

Visualize Tomorrow: Neural Networks in Vision

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.

Certification Duration: 40 hours (5 Days)

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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.

Modules

10

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Certification Modules

  1. Course IntroductionPreview
  1. 1.1 Introduction to Neural Networks
  2. 1.2 Neural Network Architecture
  3. 1.3 Hands-on: Implement a Basic Neural Network
  1. 2.1 Hyperparameter Tuning
  2. 2.2 Optimization Algorithms
  3. 2.3 Regularization Techniques
  4. 2.4 Hands-on: Hyperparameter Tuning and Optimization
  1. 3.1 Key NLP Concepts
  2. 3.2 NLP-Specific Architectures
  3. 3.3 Hands-on: Implementing an NLP Model
  1. 4.1 Key Computer Vision Concepts
  2. 4.2 Computer Vision-Specific Architectures
  3. 4.3 Hands-on: Building a Computer Vision Model
  1. 5.1 Model Evaluation Techniques
  2. 5.2 Improving Model Performance
  3. 5.3 Hands-on: Evaluating and Optimizing AI Models
  1. 6.1 Infrastructure for AI Development
  2. 6.2 Deployment Strategies
  3. 6.3 Hands-on: Deploying an AI Model
  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
  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
  1. 9.1 AI Research Techniques
  2. 9.2 Cutting-Edge AI Design
  3. 9.3 Hands-on: Analyzing AI Research Papers
  1. 10.1 Capstone Project Presentation
  2. 10.2 Course Review and Future Directions
  3. 10.3 Hands-on: Capstone Project Development

Certification Modules

  1. Course IntroductionPreview
  1. 1.1 Introduction to Neural Networks
  2. 1.2 Neural Network Architecture
  3. 1.3 Hands-on: Implement a Basic Neural Network
  1. 2.1 Hyperparameter Tuning
  2. 2.2 Optimization Algorithms
  3. 2.3 Regularization Techniques
  4. 2.4 Hands-on: Hyperparameter Tuning and Optimization
  1. 3.1 Key NLP Concepts
  2. 3.2 NLP-Specific Architectures
  3. 3.3 Hands-on: Implementing an NLP Model
  1. 4.1 Key Computer Vision Concepts
  2. 4.2 Computer Vision-Specific Architectures
  3. 4.3 Hands-on: Building a Computer Vision Model
  1. 5.1 Model Evaluation Techniques
  2. 5.2 Improving Model Performance
  3. 5.3 Hands-on: Evaluating and Optimizing AI Models
  1. 6.1 Infrastructure for AI Development
  2. 6.2 Deployment Strategies
  3. 6.3 Hands-on: Deploying an AI Model
  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
  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
  1. 9.1 AI Research Techniques
  2. 9.2 Cutting-Edge AI Design
  3. 9.3 Hands-on: Analyzing AI Research Papers
  1. 10.1 Capstone Project Presentation
  2. 10.2 Course Review and Future Directions
  3. 10.3 Hands-on: Capstone Project Development

Tools

AutoGluon

ChatGPT

SonarCube

Vertex AI

Exam Objectives

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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.

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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.

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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.

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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 Post-Certification

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Median Salaries

$1,20,319
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With AI Skills

$1,58,719
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% Difference

32
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AI Systems Architect

Designs and implements robust AI systems, ensuring optimal architecture, performance, and integration with existing technologies and platforms.

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AI Solutions Architecture Developer

Develops tailored AI solutions, leveraging architectural principles to meet specific business needs and technical requirements efficiently.

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AI Technology Systems Planner

Plans and strategizes the deployment of AI technologies, aligning with business goals and ensuring future scalability and innovation.

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AI Enterprise Architecture Strategist

Creates enterprise-level AI strategies, integrating AI initiatives into the broader organizational framework for long-term success and competitiveness.

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Frequently Asked Questions

The course covers fundamental concepts of neural networks, optimization techniques, and advanced AI architectures specific to natural language processing (NLP) and computer vision applications. It also includes modules on model evaluation, AI infrastructure deployment, ethics in AI, and generative AI models.

Learners will acquire advanced skills in neural networks, optimization techniques, specialized architectures for NLP and computer vision, model evaluation, performance metrics, AI infrastructure deployment, ethical AI design, generative AI models, and research-based AI design principles.

While familiarity with basic AI concepts and programming is beneficial, the course is designed to accommodate learners at various levels, offering foundational to advanced topics in AI.

The course provides insights into deploying AI models in practical settings, covering topics like model packaging, scalability assessment, integration with existing systems, and ensuring robust performance in production environments.

Graduates can pursue roles such as AI Architect, Machine Learning Engineer, AI Research Scientist, NLP Specialist, Computer Vision Engineer, and more, in several industries.

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