AI+ Architect™
AI+ Architect™
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
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.
- Course IntroductionPreview
- 1.1 Introduction to Neural Networks
- 1.2 Neural Network Architecture
- 1.3 Hands-on: Implement a Basic Neural Network
- 2.1 Hyperparameter Tuning
- 2.2 Optimization Algorithms
- 2.3 Regularization Techniques
- 2.4 Hands-on: Hyperparameter Tuning and Optimization
- 3.1 Key NLP Concepts
- 3.2 NLP-Specific Architectures
- 3.3 Hands-on: Implementing an NLP Model
- 4.1 Key Computer Vision Concepts
- 4.2 Computer Vision-Specific Architectures
- 4.3 Hands-on: Building a Computer Vision Model
- 5.1 Model Evaluation Techniques
- 5.2 Improving Model Performance
- 5.3 Hands-on: Evaluating and Optimizing AI Models
- 6.1 Infrastructure for AI Development
- 6.2 Deployment Strategies
- 6.3 Hands-on: Deploying an AI Model
- 7.1 Ethical Considerations in AI
- 7.2 Best Practices for Responsible AI Design
- 7.3 Hands-on: Analyzing Ethical Considerations in AI
- 8.1 Overview of Generative AI Models
- 8.2 Generative AI Applications in Various Domains
- 8.3 Hands-on: Exploring Generative AI Models
- 9.1 AI Research Techniques
- 9.2 Cutting-Edge AI Design
- 9.3 Hands-on: Analyzing AI Research Papers
- 10.1 Capstone Project Presentation
- 10.2 Course Review and Future Directions
- 10.3 Hands-on: Capstone Project Development
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
AutoGluon
ChatGPT
SonarCube
Vertex AI
Passing Score : 70% (35/50)
Number of Multiple Choice Question : 50 MCQs, 90 Minutes
Number of exams required for certification : 1
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%
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.
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.
FAQ
What is the duration of the AI+ Architect certification course?
The certification lasts 40 hours, typically completed over 5 days, providing an intensive learning experience.
What will I learn in the AI+ Architect certification?
You will learn advanced neural network techniques, model optimization, NLP and computer vision architectures, AI deployment infrastructure, and ethical AI design.
Who should enroll in this course?
This course is ideal for AI architects, engineers, software developers, and professionals seeking to master AI architectures and neural networks.
Do I need prior experience to enroll in the AI+ Architect course?
A foundational understanding of AI and neural networks is recommended but not required, as the course starts with core concepts.
What is the outcome after completing the AI+ Architect certification?
Participants will be equipped with both theoretical and practical knowledge to design, optimize, and implement AI architectures.
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