AI+ Engineer Practitioner™

• 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+ Engineer Practitioner™ 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 Introduction to AI Preview
• 1.2 Core Concepts and Techniques in AI Preview
• 1.3 Ethical Considerations

• 2.1 Overview of AI and its Various ApplicationsPreview
• 2.2 Introduction to AI Architecture Preview
• 2.3 Understanding the AI Development Lifecycle Preview
• 2.4 Hands-on: Setting up a Basic AI Environment

• 3.1 Basics of Neural Networks Preview
• 3.2 Activation Functions and Their Role Preview
• 3.3 Backpropagation and Optimization Algorithms
• 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

• 4.1 Introduction to Neural Networks in Image Processing
• 4.2 Neural Networks for Sequential Data
• 4.3 Practical Implementation of Neural Networks

• 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

• 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

• 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

• 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

• 9.1 Overview of GUI-based AI Applications
• 9.2 Web-based Framework
• 9.3 Desktop Application Framework

• 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

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

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