AI+ Engineer™

• AI & Software Engineers: Enhance your development skills by mastering AI techniques and designing advanced AI systems.
• Machine Learning Enthusiasts: Apply deep learning, neural networks, and NLP techniques to real-world AI challenges.
• Data Scientists: Strengthen your AI toolkit with engineering techniques for building and deploying scalable AI solutions.
• IT Specialists & System Architects: Integrate AI solutions into existing infrastructures, optimizing performance and scalability.
• Students & New Graduates: Develop in-demand AI engineering skills and prepare for a successful career in the rapidly growing AI field.

• Master AI System Design:Develop the skills to design, implement, and optimize advanced AI systems for real-world applications.
• Build Scalable AI Solutions:Learn how to create scalable AI solutions for industries like technology, finance, and healthcare.
• Tackle Complex Engineering Challenges:This certification ensures you’re equipped to solve challenges in AI architecture, neural networks, and NLP.
• Contribute to AI-Driven Innovations:Certified AI+ Engineers develop cutting-edge AI solutions that enhance business operations and drive future innovations.
• Advance Your Career in AI Engineering: As demand for skilled AI engineers rises, this certification offers a competitive advantage in the job market.

Inhoud

• Course Introduction Preview

1. 1.1 Introduction to AI Preview
2. 1.2 Core Concepts and Techniques in AI Preview
3. 1.3 Ethical Considerations
Module 2: Introduction to AI Architecture
1. 2.1 Overview of AI and its Various ApplicationsPreview
2. 2.2 Introduction to AI Architecture Preview
3. 2.3 Understanding the AI Development Lifecycle Preview
4. 2.4 Hands-on: Setting up a Basic AI Environment
Module 3: Fundamentals of Neural Networks
1. 3.1 Basics of Neural Networks Preview
2. 3.2 Activation Functions and Their Role Preview
3. 3.3 Backpropagation and Optimization Algorithms
4. 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
• 1. Understanding AI Agents
• 2. Case Studies
• 3. Hands-On Practice with AI Agents
Tools you will explore
• TensorFlow
• Hugging Face Transformers
• Jenkins
• TensorFlow Hub

Lesmethode

Instructor-led OR Self-paced course + Official exam + Digital badge

Kenmerken

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

Voorkennis

• AI+ Data™  or AI+ Developer™ course should be completed, basic math, computer science fundamentals, Python familiarity