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AI+ Architect Practitioner™

Course code: NPAT320

Formerly known as AI+ Architect™

Visualize Tomorrow: Neural Networks in Vision

  • Advanced AI Mastery: Delve into neural networks, natural language processing, and computer vision structures.
  • Scalable Business AI Builds: Master creating robust AI frameworks for meaningful enterprise applications.
  • Capstone Project Execution: Construct, evaluate, and launch sophisticated AI architectural solutions.
  • Career Ready Expertise: Prepares you for high-demand positions across specialized AI design and development domains.
75 EUR

91 EUR including VAT

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Course dates

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 30 hours

Language: en

Price without VAT: 285 EUR

Register

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 30 hours

Language: en

Price without VAT: 75 EUR

Register

Starting
date		 Place
Type Course
duration		 Language Price without VAT
G Upon request Self-paced 30 hours en 285 EUR Register
G Upon request Self-paced 30 hours en 75 EUR Register
G Guaranteed course

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Course description

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.

Course structure

Certification Overview

Module 1: Fundamentals of Neural Networks

  • 1.1 Introduction to Neural Networks
  • 1.2 Neural Network Architecture
  • 1.3 Hands-on: Implement a Basic Neural Network

Module 2: Neural Network Optimization

  • 2.1 Hyperparameter Tuning
  • 2.2 Optimization Algorithms
  • 2.3 Regularization Techniques
  • 2.4 Hands-on: Hyperparameter Tuning and Optimization

Module 3: Neural Network Architectures for NLP

  • 3.1 Key NLP Concepts
  • 3.2 NLP-Specific Architectures
  • 3.3 Hands-on: Implementing an NLP Model

Module 4: Neural Network Architectures for Computer Vision

  • 4.1 Key Computer Vision Concepts
  • 4.2 Computer Vision-Specific Architectures
  • 4.3 Hands-on: Building a Computer Vision Model

Module 5: Model Evaluation and Performance Metrics

  • 5.1 Model Evaluation Techniques
  • 5.2 Improving Model Performance
  • 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: AI Infrastructure and Deployment

  • 6.1 Infrastructure for AI Development
  • 6.2 Deployment Strategies
  • 6.3 Hands-on: Deploying an AI Model

Module 7: AI Ethics and Responsible AI Design

  • 7.1 Ethical Considerations in AI
  • 7.2 Best Practices for Responsible AI Design
  • 7.3 Hands-on: Analyzing Ethical Considerations in AI

Module 8: Generative AI Models

  • 8.1 Overview of Generative AI Models
  • 8.2 Generative AI Applications in Various Domains
  • 8.3 Hands-on: Exploring Generative AI Models

Module 9: Research-Based AI Design

  • 9.1 AI Research Techniques
  • 9.2 Cutting-Edge AI Design
  • 9.3 Hands-on: Analyzing AI Research Papers

Module 10: Capstone Project and Course Review

  • 10.1 Capstone Project Presentation
  • 10.2 Course Review and Future Directions
  • 10.3 Hands-on: Capstone Project Development

Optional Module: AI Agents for Architect

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

Prerequisites

  • Foundational understanding of neural networks, covering their optimization techniques and architectural design for practical applications
  • Capability to assess models through diverse performance indicators to verify accuracy and dependability
  • Readiness to explore AI infrastructure frameworks and deployment workflows to effectively implement and sustain AI systems

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