Courses AI certifications (AI CERTs®)

AI+ Engineer Practitioner™

  • Price 279 EUR + VAT
  • E-learning Start any time
  • Duration 40 hours of content, self-paced
  • Language English
  • Topic AI Development

How you can take it

  • E-learning Start any time · 279 EUR + VAT Order
  • Open course Not available in this format
  • Private in-house Any of our courses for your team Request a quote

Overview

What the course covers

The course, the materials and the exam are in English. The description below is the original AI CERTs® text.

  • 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

Prerequisites

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

Audience

Who is it for

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.

In the price

What the price includes

  • Self-paced online course with the full learning material
  • The official AI CERTs® exam
  • Digital badge after passing the exam

Course content

Syllabus

Course Overview
  • Course Introduction Preview
Module 1: Foundations of Artificial Intelligence
  • 1.1 Introduction to AI Preview
  • 1.2 Core Concepts and Techniques in AI Preview
  • 1.3 Ethical Considerations
Module 2: Introduction to AI Architecture
  • 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
Module 3: Fundamentals of Neural Networks
  • 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
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

Exam

About the exam

  • 50 Questions
  • 90 min Time limit
  • 70% Pass mark
  • Online proctored exam

Related courses

You may also like

All courses

AI+ Engineer Practitioner™

Full AI Stack: Learn AI architecture, LLMs, NLP, and neural networks; Tool Proficiency: Includes Transfer Learning with Hugging Face and GUI design…

You are here

AI-Enabled Internal Audit™ Certificate Program

Learn to use AI for smarter, faster, and more effective internal audits. Prerequisites A general understanding of internal audit concepts and the…

View course

AI+ Security Compliance Practitioner™

The AI+ Security Compliance Practitioner™ is an advanced certification that merges the fundamental principles of cybersecurity compliance with the…

View course

AI+ Security Expert™

This certification validates intermediate-level knowledge of AI-driven cybersecurity concepts and assesses competency in applying security controls…

View course

Private training for your team?

Any of our courses can be delivered as private, in-company training, tailored to your organisation, in English or Hungarian.

Request a quote
279 EUR + VAT E-learning
Choose a format