Opleiding: AI+ Engineer Practitioner™ eLearning

Formerly known as AI+ Engineer™

Innovate Engineering: Leverage AI-Driven Smart Solutions

• Full AI Stack: Learn AI architecture, LLMs, NLP, and neural networks

• Tool Proficiency: Includes Transfer Learning with Hugging Face and GUI design

• Deployment Focus: Build real AI systems and manage communication pipelines

• Practical Mastery: Gain the skills to engineer scalable AI solutions for innovation

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.

• 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

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.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.