AI+ Engineer (AT-330 Self-Paced Training)
Ziele der Schulung
The AI+ Engineer™ certification equips participants with a comprehensive understanding of Artificial Intelligence (AI) principles, advanced engineering techniques, and practical applications. It is tailored for learners who want to master AI architecture, neural networks, Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and cutting-edge tools like Transfer Learning with Hugging Face. This program focuses on applying AI in real-world scenarios—building models, deploying solutions, and understanding how AI can make a measurable impact in industry. What Will You Learn? You will gain hands-on skills in designing scalable AI systems, implementing advanced neural network architectures, fine-tuning large language models, and deploying AI solutions with communication and deployment pipelines.
Zielgruppe Seminar
- AI & Software Engineers looking to enhance development skills with advanced AI techniques.
- Machine Learning Enthusiasts aiming to apply deep learning, neural networks, and NLP in practice.
- Data Scientists seeking to strengthen their AI engineering toolkit.
- IT Specialists & System Architects integrating AI into existing infrastructures.
- Students & New Graduates preparing for careers in AI engineering.
Voraussetzungen
- Completion of AI+ Data™ or AI+ Developer™ course.
- Basic understanding of Python programming.
- Familiarity with high-school algebra and basic statistics.
- Understanding of fundamental programming concepts (variables, functions, loops, data structures).
Seminarinhalt
Module 1: Foundations of Artificial Intelligence
- 1.1 Introduction to AI
- 1.2 Core Concepts and Techniques in AI
- 1.3 Ethical Considerations
Module 2: Introduction to AI Architecture
- 2.1 Overview of AI and its Various Applications
- 2.2 Introduction to AI Architecture
- 2.3 Understanding the AI Development Lifecycle
- 2.4 Hands-on: Setting up a Basic AI Environment
Module 3: Fundamentals of Neural Networks
- 3.1 Basics of Neural Networks
- 3.2 Activation Functions and Their Role
- 3.3 Backpropagation and Optimization Algorithms
- 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
Module 4: Applications of Neural Networks
- 4.1 Introduction to Neural Networks in Image Processing
- 4.2 Neural Networks for Sequential Data
- 4.3 Practical Implementation of Neural Networks
Module 5: Significance of Large Language Models (LLM)
- 5.1 Exploring Large Language Models
- 5.2 Popular Large Language Models
- 5.3 Practical Finetuning of Language Models
- 5.4 Hands-on: Practical Finetuning for Text Classification
Module 6: Application of Generative AI
- 6.1 Introduction to Generative Adversarial Networks (GANs)
- 6.2 Applications of Variational Autoencoders (VAEs)
- 6.3 Generating Realistic Data Using Generative Models
- 6.4 Hands-on: Implementing Generative Models for Image Synthesis
Module 7: Natural Language Processing
- 7.1 NLP in Real-world Scenarios
- 7.2 Attention Mechanisms and Practical Use of Transformers
- 7.3 In-depth Understanding of BERT for Practical NLP Tasks
- 7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models
Module 8: Transfer Learning with Hugging Face
- 8.1 Overview of Transfer Learning in AI
- 8.2 Transfer Learning Strategies and Techniques
- 8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks
Module 9: Crafting Sophisticated GUIs for AI Solutions
- 9.1 Overview of GUI-based AI Applications
- 9.2 Web-based Framework
- 9.3 Desktop Application Framework
Module 10: AI Communication and Deployment Pipeline
- 10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
- 10.2 Building a Deployment Pipeline for AI Models
- 10.3 Developing Prototypes Based on Client Requirements
- 10.4 Hands-on: Deployment
Optional Module: AI Agents for Engineering
- Understanding AI Agents
- Case Studies
- Hands-On Practice with AI Agents
Hinweise
Prüfung und Zertifizierung
- Duration: 90 minutes
- Passing Score: 70% (35/50)
- Format: 50 multiple-choice/multiple-response questions
- Delivery Method: Online via proctored exam platform (flexible scheduling)
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