Opleidingen
56.682
resultaten
AI+ Game Design Practitioner™
Build real-world gaming projects using cutting-edge AI technologies.
Develop the skills to create next-generation gaming experiences by combining artificial intelligence with modern game design. Learn how to build adaptive storytelling systems, intelligent NPCs, and AI-driven gameplay that deliver immersive, data-enhanced player experiences. Gain hands-on experience through real-world projects involving character behavior modeling, predictive player analytics, and intelligent game mechanics that strengthen both creativity and technical expertise. Earn a globally recognized certification that validates your AI gaming skills and enhances your professional credibility. Prepare for exciting career opportunities in game development, AI simulation, virtual production, and interactive entertainment while staying ahead with the latest advances in generative AI, immersive simulations, and intelligent gameplay systems.
Module 1: Introduction to AI in Games
1.1 What is AI?
1.2 Evolution of AI in the Gaming Industry
1.3 Types of AI in Games
1.4 Benefits, Challenges, and Innovations in Game AI
Module 2: Game Design Principles using AI
2.1 Understanding Game Mechanics and Player Experience
2.2 Role of AI in Gameplay and Narrative Design
2.3 Designing Game Environments for AI Interaction
2.4 AI-Driven Behavior vs Traditional Scripted Logic
2.5 Case Study: Dynamic AI and Narrative Adaptation in Middle earth: Shadow of Mordor
2.6 Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction
Module 3: Foundations of AI in Gaming
3.1 Core AI Concepts for Gaming
3.2 Search Algorithms and Pathfinding
3.3 AI Behavior Modeling and Procedural Content Generation (PCG)
3.4 Introduction to Machine Learning and Reinforcement Learning
3.5 Case Study: AI in Minecraft — Procedural Content Generation and Agent Navigation
3.6 Hands-On: Implementing A* Pathfinding and FSM for NPC Behavior
Module 4: Reinforcement Learning Fundamentals
4.1 Core Concepts: States, Actions, Rewards, Policies, Q-Learning:
4.2 Exploration versus Exploitation in Learning Systems:
4.3 Overview of Deep Q Networks (DQN) and Policy Gradient Methods
4.4 Case Study: Reinforcement Learning in DeepMind’s AlphaGo
4.5 Hands-On: Train a Reinforcement Learning Model on OpenAI Gym’s GridWorld
Module 5: Planning and Decision Making in Games
5.1 Minimax Algorithm and Alpha-Beta Pruning
5.2 Monte Carlo Tree Search (MCTS)
5.3 Applications in Board Games and Real-Time Strategy (RTS) Games
5.4 Case Study: Strategic AI in StarCraft II – Combining Planning Algorithms for Real-Time Strategy
5.5 Hands-on Implementation: Guides on implementing the Minimax algorithm for Tic-Tac-Toe
Module 6: AI Techniques in 2D/3D Virtual Gaming Environments Basic
6.1 Overview of 2D and 3D Game Environments
6.2 Environment Representation Techniques
6.3 Navigation and Pathfinding in 2D/3D Spaces
6.4 Interaction and Behavior Systems in Virtual Environments
6.5 Case Study: Navigation and Interaction AI in The Legend of Zelda: Breath of the Wild
6.6 Hands-On: Building Basic Navigation and Interaction in 2D and 3D Game Environments
Module 7: Adaptive Systems and Dynamic Difficulty
7.1 Adaptive Systems Overview
7.2 Dynamic Difficulty Adjustment (DDA) Principles
7.3 Adaptive Storytelling, Personalization, and Player Profiling
7.4 AI Techniques in Adaptive Systems
7.5 Implementation Strategies and Tools
7.6 Case Study: Dynamic Enemy Management and Replayability with Left 4 Dead’s AI Director
7.7 Hands-On: Developing an Adaptive Dynamic Difficulty System in Unity
Module 8: Future of AI in Gaming
8.1 Generalist AI Agents and Transfer Learning
8.2 AI-Powered Game Design and Testing Tools
8.3 Ethical Considerations and AI Transparency
8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching
Module 9: Capstone Project Tools you will explore
Unity ML-Agents
TensorFlow
PyTorch
Python
OpenAI Gym
Blender
NVIDIA DeepStream
Reinforcement Learning Frameworks
Natural Language Processing Libraries
Computer Vision SDKs
Game Data Analytics Tools
Behavior Tree Editors
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Game Design Practitioner™ eLearning
Build real-world gaming projects using cutting-edge AI technologies.
Develop the skills to create next-generation gaming experiences by combining artificial intelligence with modern game design. Learn how to build adaptive storytelling systems, intelligent NPCs, and AI-driven gameplay that deliver immersive, data-enhanced player experiences. Gain hands-on experience through real-world projects involving character behavior modeling, predictive player analytics, and intelligent game mechanics that strengthen both creativity and technical expertise. Earn a globally recognized certification that validates your AI gaming skills and enhances your professional credibility. Prepare for exciting career opportunities in game development, AI simulation, virtual production, and interactive entertainment while staying ahead with the latest advances in generative AI, immersive simulations, and intelligent gameplay systems.
Module 1: Introduction to AI in Games
1.1 What is AI?
1.2 Evolution of AI in the Gaming Industry
1.3 Types of AI in Games
1.4 Benefits, Challenges, and Innovations in Game AI
Module 2: Game Design Principles using AI
2.1 Understanding Game Mechanics and Player Experience
2.2 Role of AI in Gameplay and Narrative Design
2.3 Designing Game Environments for AI Interaction
2.4 AI-Driven Behavior vs Traditional Scripted Logic
2.5 Case Study: Dynamic AI and Narrative Adaptation in Middle earth: Shadow of Mordor
2.6 Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction
Module 3: Foundations of AI in Gaming
3.1 Core AI Concepts for Gaming
3.2 Search Algorithms and Pathfinding
3.3 AI Behavior Modeling and Procedural Content Generation (PCG)
3.4 Introduction to Machine Learning and Reinforcement Learning
3.5 Case Study: AI in Minecraft — Procedural Content Generation and Agent Navigation
3.6 Hands-On: Implementing A* Pathfinding and FSM for NPC Behavior
Module 4: Reinforcement Learning Fundamentals
4.1 Core Concepts: States, Actions, Rewards, Policies, Q-Learning:
4.2 Exploration versus Exploitation in Learning Systems:
4.3 Overview of Deep Q Networks (DQN) and Policy Gradient Methods
4.4 Case Study: Reinforcement Learning in DeepMind’s AlphaGo
4.5 Hands-On: Train a Reinforcement Learning Model on OpenAI Gym’s GridWorld
Module 5: Planning and Decision Making in Games
5.1 Minimax Algorithm and Alpha-Beta Pruning
5.2 Monte Carlo Tree Search (MCTS)
5.3 Applications in Board Games and Real-Time Strategy (RTS) Games
5.4 Case Study: Strategic AI in StarCraft II – Combining Planning Algorithms for Real-Time Strategy
5.5 Hands-on Implementation: Guides on implementing the Minimax algorithm for Tic-Tac-Toe
Module 6: AI Techniques in 2D/3D Virtual Gaming Environments Basic
6.1 Overview of 2D and 3D Game Environments
6.2 Environment Representation Techniques
6.3 Navigation and Pathfinding in 2D/3D Spaces
6.4 Interaction and Behavior Systems in Virtual Environments
6.5 Case Study: Navigation and Interaction AI in The Legend of Zelda: Breath of the Wild
6.6 Hands-On: Building Basic Navigation and Interaction in 2D and 3D Game Environments
Module 7: Adaptive Systems and Dynamic Difficulty
7.1 Adaptive Systems Overview
7.2 Dynamic Difficulty Adjustment (DDA) Principles
7.3 Adaptive Storytelling, Personalization, and Player Profiling
7.4 AI Techniques in Adaptive Systems
7.5 Implementation Strategies and Tools
7.6 Case Study: Dynamic Enemy Management and Replayability with Left 4 Dead’s AI Director
7.7 Hands-On: Developing an Adaptive Dynamic Difficulty System in Unity
Module 8: Future of AI in Gaming
8.1 Generalist AI Agents and Transfer Learning
8.2 AI-Powered Game Design and Testing Tools
8.3 Ethical Considerations and AI Transparency
8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching
Module 9: Capstone Project Tools you will explore
Unity ML-Agents
TensorFlow
PyTorch
Python
OpenAI Gym
Blender
NVIDIA DeepStream
Reinforcement Learning Frameworks
Natural Language Processing Libraries
Computer Vision SDKs
Game Data Analytics Tools
Behavior Tree Editors
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Game Design Agent Specialty™
Empower creators with AI+ Game Design Agent Specialty™ to craft intelligent, dynamic, and immersive gaming experiences.
Become a leader in the next generation of game development by combining creativity with the power of artificial intelligence. This program equips you with the skills to design immersive gaming experiences using procedural content generation, adaptive storytelling, intelligent NPC behavior, and AI-driven gameplay mechanics. Gain hands-on experience through practical projects focused on level generation, character modeling, player behavior analysis, and game experience optimization. Earn a globally recognized certification that validates your expertise in applying AI within modern game development. Discover career opportunities in game studios, simulation engineering, interactive media, and entertainment technology while building future-ready skills in generative AI, autonomous systems, and adaptive game design that prepare you for the rapidly evolving gaming industry.
Module 1: Understanding AI Agents
1.1 What are AI Agents?
1.2 Agent Architectures and Environments
1.3 Decision Making and Behavior Basics
1.4 Introduction to Multi-Agent Systems
1.5 Case Study: Pac-Man Ghost AI
1.6 Hands On: Build a Basic Reactive AI Agent Navigating a Simple Environment Using Pygame
Module 2: Introduction to AI Game Agent
2.1 What is an AI Game Agent?
2.2 Key Components of AI Game Agent
2.3 Agent Architectures
2.4 AI Game Agent Behaviors
2.5 Case Study: Racing Games (e.g., Mario Kart, Forza Horizon)
2.6 Hands-On: Creating a Simple Box Movement Game in Playcanvas
Module 3: Reinforcement Learning in Game Design
3.1 Basics of Reinforcement Learning
3.2 Key Algorithms: Q-Learning and SARSA
3.3 Applying RL to Game Agents
3.4 Challenges and Solutions in Game-based RL
3.5 Case Study: AlphaZero in Games: Mastering Chess, Shogi, and Go through Self-Play and Reinforcement Learning
3.6 Hands On: Train a simple RL agent in OpenAI Gym environment
Module 4: AI for NPCs and Pathfinding
4.1 Understanding NPCs as AI Agents
4.2 Simple AI Techniques for NPCs
4.3 Pathfinding Algorithms
4.4 Obstacle Avoidance and Movement Optimization
4.5 Case Study
4.6 Hands-On
Module 5: AI for Strategic Decision-Making
5.1 Decision Trees and Minimax for Game AI
5.2 Monte Carlo Tree Search (MCTS) for AI Agent
5.3 Utility-Based Decision Making for Game AI
5.4 AI in Real-Time Strategy (RTS) Games
5.5 Case Study: StarCraft II AI by DeepMind
5.6 Hands-On: Implement a Basic MCTS Agent for Tic-Tac-Toe Using Pygame
Module 6: AI Game Agent in 3D Virtual Environments
6.1 3D Environment Representation and Challenges for AI Agents
6.2 Navigation Mesh Generation for AI Agents in 3D
6.3 Complex Agent Behaviors in 3D Worlds
6.4 Case Study: The Last of Us
6.5 Hands On: Develop a 3D AI Agent with Navigation and Interaction in Unity Using NavMesh and C#
Module 7: Future Trends in AI Game Design
7.1 Current and Future AI Trends
7.2 The Future of Generalist AI in Gaming
7.3 Case Study
Module 8: Capstone Project
8.1. Task Description
8.2. Practical Implementation
8.3. Testing and Debugging
8.4. Hands-on
Tools you will explore
Unity ML-Agents
PyTorch
TensorFlow
Python
OpenAI Gym
Blender
Godot Engine
NVIDIA Omniverse
Hugging Face Transformers
Reinforcement Learning Frameworks
Natural Language Processing Libraries
Computer Vision SDKs
Game Analytics Tools
Behavior Tree Editors
Procedural Generation Tools
Speech and Emotion Recognition APIs
AI Animation Systems
3D Simulation Platforms
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Game Design Agent Specialty™ eLearning
Empower creators with AI+ Game Design Agent Specialty™ to craft intelligent, dynamic, and immersive gaming experiences.
Become a leader in the next generation of game development by combining creativity with the power of artificial intelligence. This program equips you with the skills to design immersive gaming experiences using procedural content generation, adaptive storytelling, intelligent NPC behavior, and AI-driven gameplay mechanics. Gain hands-on experience through practical projects focused on level generation, character modeling, player behavior analysis, and game experience optimization. Earn a globally recognized certification that validates your expertise in applying AI within modern game development. Discover career opportunities in game studios, simulation engineering, interactive media, and entertainment technology while building future-ready skills in generative AI, autonomous systems, and adaptive game design that prepare you for the rapidly evolving gaming industry.
Module 1: Understanding AI Agents
1.1 What are AI Agents?
1.2 Agent Architectures and Environments
1.3 Decision Making and Behavior Basics
1.4 Introduction to Multi-Agent Systems
1.5 Case Study: Pac-Man Ghost AI
1.6 Hands On: Build a Basic Reactive AI Agent Navigating a Simple Environment Using Pygame
Module 2: Introduction to AI Game Agent
2.1 What is an AI Game Agent?
2.2 Key Components of AI Game Agent
2.3 Agent Architectures
2.4 AI Game Agent Behaviors
2.5 Case Study: Racing Games (e.g., Mario Kart, Forza Horizon)
2.6 Hands-On: Creating a Simple Box Movement Game in Playcanvas
Module 3: Reinforcement Learning in Game Design
3.1 Basics of Reinforcement Learning
3.2 Key Algorithms: Q-Learning and SARSA
3.3 Applying RL to Game Agents
3.4 Challenges and Solutions in Game-based RL
3.5 Case Study: AlphaZero in Games: Mastering Chess, Shogi, and Go through Self-Play and Reinforcement Learning
3.6 Hands On: Train a simple RL agent in OpenAI Gym environment
Module 4: AI for NPCs and Pathfinding
4.1 Understanding NPCs as AI Agents
4.2 Simple AI Techniques for NPCs
4.3 Pathfinding Algorithms
4.4 Obstacle Avoidance and Movement Optimization
4.5 Case Study
4.6 Hands-On
Module 5: AI for Strategic Decision-Making
5.1 Decision Trees and Minimax for Game AI
5.2 Monte Carlo Tree Search (MCTS) for AI Agent
5.3 Utility-Based Decision Making for Game AI
5.4 AI in Real-Time Strategy (RTS) Games
5.5 Case Study: StarCraft II AI by DeepMind
5.6 Hands-On: Implement a Basic MCTS Agent for Tic-Tac-Toe Using Pygame
Module 6: AI Game Agent in 3D Virtual Environments
6.1 3D Environment Representation and Challenges for AI Agents
6.2 Navigation Mesh Generation for AI Agents in 3D
6.3 Complex Agent Behaviors in 3D Worlds
6.4 Case Study: The Last of Us
6.5 Hands On: Develop a 3D AI Agent with Navigation and Interaction in Unity Using NavMesh and C#
Module 7: Future Trends in AI Game Design
7.1 Current and Future AI Trends
7.2 The Future of Generalist AI in Gaming
7.3 Case Study
Module 8: Capstone Project
8.1. Task Description
8.2. Practical Implementation
8.3. Testing and Debugging
8.4. Hands-on
Tools you will explore
Unity ML-Agents
PyTorch
TensorFlow
Python
OpenAI Gym
Blender
Godot Engine
NVIDIA Omniverse
Hugging Face Transformers
Reinforcement Learning Frameworks
Natural Language Processing Libraries
Computer Vision SDKs
Game Analytics Tools
Behavior Tree Editors
Procedural Generation Tools
Speech and Emotion Recognition APIs
AI Animation Systems
3D Simulation Platforms
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Audio Practitioner™
Formerly known as AI+ Audio™
Experience the power of AI in Audio to reinvent music production, elevate sound design, and craft immersive auditory experiences.
Empower Audio Innovation with AI: Creative, Practical, Transformative
Beginner-Friendly Learning: Perfect for newcomers eager to explore AI-powered audio, covering essential concepts with ease
Comprehensive Skill Building: Includes speech processing, sound enhancement, voice synthesis, and real-world audio AI applications
Industry-Ready Expertise: Understand how AI is reshaping music, media, entertainment, and communication sectors
Hands-On Direction: Provides practical frameworks and guided exercises to help you create, analyse, and optimise audio using AI
Module 1: Introduction to AI and Sound
1.1 What is AI?
1.2 AI in Daily Life: Audio Examples
1.3 Basics of Sound Waves, Amplitude, Frequency
1.4 Digital Audio Fundamentals
Module 2: Harnessing AI Across Audio Domains
2.1 AI for Audio Enhancement and Restoration
2.2 AI for Audio Accessibility and Personalization
2.3 AI in Speech and Voice Technologies
2.4 Popular Audio Libraries: Librosa, PyAudio
2.5 Use Case:AI-Driven Real-Time Captioning and Translation for Live Events
2.6 Case Study:Personalized Hearing Aid Adaptation Using AI and Smart Earbuds
2.7 Hands-on: Voice Emotion Detection using Deepgram’s Voice AI Platform
Module 3: Machine Learning & AI for Audio
3.1 Machine Learning Models for Audio Applications
3.2 Deep Learning & Advanced AI Techniques for Audio
3.3 Audio-Specific Architectures: CNNs, RNNs, Transformers
3.4 Transfer Learning in Audio AI
3.5 Use Case: Speech-to-Text Transcription for Medical Records
3.6 Case Study: AI-powered Music Generation with Deep Learning
3.7 Hands-on: Build a Speech-to-Text Model Using TensorFlow
Module 4: Speech Recognition & Text-to-Speech
4.1 Fundamentals of Speech Recognition & Phonetics
4.2 API-based ASR Solutions
4.3 Building Custom ASR Models with Transformers
4.4 Introduction to TTS & Voice Cloning
4.5 Use Case: Automating Meeting Transcriptions with Google Speech-to-Text API
4.6 Case Study: Custom Transformer-based ASR Model for Multilingual Customer Support
4.7 Hands-on: Transcribe audio with an ASR API; generate speech from text
Module 5: Audio Enhancement & Noise Reduction
5.1 Common Audio Issues
5.2 AI-based Noise Filtering & Enhancement
5.3 Use Cases: Enhancing Audio Quality for Remote Work Calls Using AI Noise Reduction
5.4 Case Study: Krisp’s AI-powered Noise Cancellation in Podcast Production
5.5 Hands-on: Use Krisp or Adobe Enhance Speech to clean noisy audio
Module 6: Emotion & Sentiment Detection from Audio
6.1 Introduction to Emotion Detection
6.2 AI Models for Emotion Detection: RNNs, LSTMs, CNNs
6.3 Challenges: Bias, Multilingual Contexts, Reliability
6.4 Use Case: Enhancing Customer Service with Emotion Detection from Speech
6.5 Case Study: IBM Watson Tone Analyzer for Real-Time Emotion Recognition
6.6 Hands-on: Use IBM Watson Tone Analyzer or similar APIs to analyze speech samples
Module 7: Ethical and Privacy Considerations
7.1 Deepfakes and Voice Cloning Risks
7.2 Privacy and Data Security
7.3 Bias and Fairness in Audio AI
7.4 Use Case: Implementing Ethical Voice Data Collection and Consent Management
7.5 Case Study: Addressing Bias and Privacy in Audio AI under GDPR Compliance
7.6 Hands-on: Detect fake audio clips; create an ethical AI checklist
Module 8: Advanced Applications & Future Trends
8.1 Sound Event Detection & Classification
8.2 Audio Search and Indexing
8.3 Innovations: Multimodal AI, Edge Computing, 3D Audio
8.4 Emerging Careers in Audio AI
Tools you will explore
TensorFlow Audio Recognition
PyTorch Sound Classification
Librosa
OpenAI Jukebox
Google Magenta Studio
Audacity AI Plugins
Adobe Podcast AI Tools
AIVA
Wav2Vec
SpeechBrain
JUCE Framework
FL Studio with AI Integrations
Logic Pro Smart Tools
Sonible Smart EQ
Spotify Audio Analysis API
NVIDIA Riva Speech SDK
Deep Learning for Audio Toolkit
AudioLDM
Sound Design Automation Tools
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.
Included Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Audio Practitioner™ eLearning
Formerly known as AI+ Audio™
Experience the power of AI in Audio to reinvent music production, elevate sound design, and craft immersive auditory experiences.
Empower Audio Innovation with AI: Creative, Practical, Transformative
Beginner-Friendly Learning: Perfect for newcomers eager to explore AI-powered audio, covering essential concepts with ease
Comprehensive Skill Building: Includes speech processing, sound enhancement, voice synthesis, and real-world audio AI applications
Industry-Ready Expertise: Understand how AI is reshaping music, media, entertainment, and communication sectors
Hands-On Direction: Provides practical frameworks and guided exercises to help you create, analyse, and optimise audio using AI
Module 1: Introduction to AI and Sound
1.1 What is AI?
1.2 AI in Daily Life: Audio Examples
1.3 Basics of Sound Waves, Amplitude, Frequency
1.4 Digital Audio Fundamentals
Module 2: Harnessing AI Across Audio Domains
2.1 AI for Audio Enhancement and Restoration
2.2 AI for Audio Accessibility and Personalization
2.3 AI in Speech and Voice Technologies
2.4 Popular Audio Libraries: Librosa, PyAudio
2.5 Use Case:AI-Driven Real-Time Captioning and Translation for Live Events
2.6 Case Study:Personalized Hearing Aid Adaptation Using AI and Smart Earbuds
2.7 Hands-on: Voice Emotion Detection using Deepgram’s Voice AI Platform
Module 3: Machine Learning & AI for Audio
3.1 Machine Learning Models for Audio Applications
3.2 Deep Learning & Advanced AI Techniques for Audio
3.3 Audio-Specific Architectures: CNNs, RNNs, Transformers
3.4 Transfer Learning in Audio AI
3.5 Use Case: Speech-to-Text Transcription for Medical Records
3.6 Case Study: AI-powered Music Generation with Deep Learning
3.7 Hands-on: Build a Speech-to-Text Model Using TensorFlow
Module 4: Speech Recognition & Text-to-Speech
4.1 Fundamentals of Speech Recognition & Phonetics
4.2 API-based ASR Solutions
4.3 Building Custom ASR Models with Transformers
4.4 Introduction to TTS & Voice Cloning
4.5 Use Case: Automating Meeting Transcriptions with Google Speech-to-Text API
4.6 Case Study: Custom Transformer-based ASR Model for Multilingual Customer Support
4.7 Hands-on: Transcribe audio with an ASR API; generate speech from text
Module 5: Audio Enhancement & Noise Reduction
5.1 Common Audio Issues
5.2 AI-based Noise Filtering & Enhancement
5.3 Use Cases: Enhancing Audio Quality for Remote Work Calls Using AI Noise Reduction
5.4 Case Study: Krisp’s AI-powered Noise Cancellation in Podcast Production
5.5 Hands-on: Use Krisp or Adobe Enhance Speech to clean noisy audio
Module 6: Emotion & Sentiment Detection from Audio
6.1 Introduction to Emotion Detection
6.2 AI Models for Emotion Detection: RNNs, LSTMs, CNNs
6.3 Challenges: Bias, Multilingual Contexts, Reliability
6.4 Use Case: Enhancing Customer Service with Emotion Detection from Speech
6.5 Case Study: IBM Watson Tone Analyzer for Real-Time Emotion Recognition
6.6 Hands-on: Use IBM Watson Tone Analyzer or similar APIs to analyze speech samples
Module 7: Ethical and Privacy Considerations
7.1 Deepfakes and Voice Cloning Risks
7.2 Privacy and Data Security
7.3 Bias and Fairness in Audio AI
7.4 Use Case: Implementing Ethical Voice Data Collection and Consent Management
7.5 Case Study: Addressing Bias and Privacy in Audio AI under GDPR Compliance
7.6 Hands-on: Detect fake audio clips; create an ethical AI checklist
Module 8: Advanced Applications & Future Trends
8.1 Sound Event Detection & Classification
8.2 Audio Search and Indexing
8.3 Innovations: Multimodal AI, Edge Computing, 3D Audio
8.4 Emerging Careers in Audio AI
Tools you will explore
TensorFlow Audio Recognition
PyTorch Sound Classification
Librosa
OpenAI Jukebox
Google Magenta Studio
Audacity AI Plugins
Adobe Podcast AI Tools
AIVA
Wav2Vec
SpeechBrain
JUCE Framework
FL Studio with AI Integrations
Logic Pro Smart Tools
Sonible Smart EQ
Spotify Audio Analysis API
NVIDIA Riva Speech SDK
Deep Learning for Audio Toolkit
AudioLDM
Sound Design Automation Tools
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.
Included Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Video Practitioner™
Embrace the future of AI in video to inspire innovation and craft immersive visual experiences
Beginner-Friendly Pathway: A perfect starting point for learners exploring AI-driven video creation, editing, and automation
End-to-End Mastery: Covers AI video fundamentals, advanced tools, generative video workflows, and responsible content creation
Industry-Aligned Skills: Understand how AI video technologies shape marketing, education, entertainment, and business communication
Practical Execution: Provides guided exercises, templates, and workflows to help you produce professional-quality AI-powered videos confidently
Module 1: Foundation of AI in Video Integration
1.1 Basics of Video Processing
1.2 Introduction to AI in Video
1.3 Toolkits and Framework
1.4 Use Case: AI-enhanced Video Compression for Streaming Platforms
1.5 Case Study: YouTube’s AI-Driven Transcoding System
Module 2: Preparing Video Data for AI
2.1 Data Preparation for AI Models
2.2 Preprocessing and Augmenting Frames
2.3 Storage and Workflow Management
2.4 Use Case: Building AI-ready Video Datasets for Autonomous Driving Applications
2.5 Case Study: Tesla’s In-house Pipeline for Labeling Driving Scenarios across Multiple Geographies using Video Footage
2.6 Hands-On: Video Annotation using CVAT Tool, and Organizing them for Model Training
Module 3: Machine Learning for Video Analysis
3.1 Video Classification and Tagging
3.2 Object Detection and Movement Tracking
3.3 Action and Behavior Recognition
3.4 Use Case: Smart Surveillance Systems Detecting Abandoned Objects in Real Time
3.5 Case Study: Dubai Smart City’s AI Implementation for Object Recognition
3.6 Hands-On: Train YOLO on Sample Security Footage to Detect and Track Objects
Module 4: Generative AI in Video
4.1 Generating Synthetic Video with GANs
4.2 AI-Driven Animation and Avatars
4.3 Ethical Use of Generative Content
4.4 Use Case: Auto-Generation of Product Explainer Videos using Avatars and Synthesized Narration
4.5 Case Study: Synthesia’s Solution Enabling Businesses to Create AI-Driven Training and Marketing Videos
4.6 Hands-On: Generate a Deepfake or AI Avatar using AKOOL, and Explore Face Alignment and Identity Swapping
Module 5: Enhancing Video with AI
5.1 Super-Resolution and Restoration
5.2 Real-Time Video Enhancement
5.3 Making Video More Inclusive
5.4 Use Case: Streaming Platforms using AI to Enhance Resolution and Reduce Latency for Mobile Users.
5.5 Case Study: DeOldify’s Impact in Reviving Historical Video Archives by Upscaling and Colorizing Black-and-White Footage.
5.6 Hands-On: Use AI4Video to Enhance a Sample Low-Resolution Black-and-White Video and Visualize Improvement
Module 6: Interactive and Immersive AI Video
6.1 AI in AR and Mixed Reality
6.2 Intelligent Video Editing
6.3 Viewer Engagement & Adaptation
6.4 Use Case: Live Sports Broadcasters using AR to Overlay Player Stats during Gameplay
6.5 Case Study: NFL and AWS Collaboration to Deliver Real-Time Performance Insights via Augmented Visuals.
6.6 Hands-On: Creating a Highlight Video from a Video Clip using Clipchamp
Module 7: AI in Video Surveillance and Compliance
7.1 Security and Monitoring Systems
7.2 Automated Content Moderation
7.3 Addressing Privacy and Ethics
7.4 Use Case: Automated Real-Time Access Control in Corporate Offices Using Facial Authentication.
7.5 Case Study: Amazon Go’s Cashier-less Stores Using Computer Vision for Security and Consumer Behavior Tracking
7.6 Hands-On: Implement Facial Detection and Access Control Simulation using OpenCV and a Basic Recognition Model
Module 8: Future of AI+ Video Practitioner™
8.1 Trends and Emerging Technologies
8.2 AI Applications by Industry
8.3 Careers and Professional Growth
Tools you will explore
TensorFlow
PyTorch
OpenCV
MediaPipe
Runway ML
Synthesia Studio
DeepFaceLab
Adobe Sensei
DaVinci Resolve Neural Engine
Runway
Pika Labs
Kaiber AI
DeepBrain AI Studio
NVIDIA Maxine SDK
Google Video AI API
FFmpeg Automation Tools
Unreal Engine with AI Plugins
Blender AI Add-ons
Stability Video Diffusion
Generative Video Editing Tools
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Video Practitioner™ eLearning
Embrace the future of AI in video to inspire innovation and craft immersive visual experiences
Beginner-Friendly Pathway: A perfect starting point for learners exploring AI-driven video creation, editing, and automation
End-to-End Mastery: Covers AI video fundamentals, advanced tools, generative video workflows, and responsible content creation
Industry-Aligned Skills: Understand how AI video technologies shape marketing, education, entertainment, and business communication
Practical Execution: Provides guided exercises, templates, and workflows to help you produce professional-quality AI-powered videos confidently
Module 1: Foundation of AI in Video Integration
1.1 Basics of Video Processing
1.2 Introduction to AI in Video
1.3 Toolkits and Framework
1.4 Use Case: AI-enhanced Video Compression for Streaming Platforms
1.5 Case Study: YouTube’s AI-Driven Transcoding System
Module 2: Preparing Video Data for AI
2.1 Data Preparation for AI Models
2.2 Preprocessing and Augmenting Frames
2.3 Storage and Workflow Management
2.4 Use Case: Building AI-ready Video Datasets for Autonomous Driving Applications
2.5 Case Study: Tesla’s In-house Pipeline for Labeling Driving Scenarios across Multiple Geographies using Video Footage
2.6 Hands-On: Video Annotation using CVAT Tool, and Organizing them for Model Training
Module 3: Machine Learning for Video Analysis
3.1 Video Classification and Tagging
3.2 Object Detection and Movement Tracking
3.3 Action and Behavior Recognition
3.4 Use Case: Smart Surveillance Systems Detecting Abandoned Objects in Real Time
3.5 Case Study: Dubai Smart City’s AI Implementation for Object Recognition
3.6 Hands-On: Train YOLO on Sample Security Footage to Detect and Track Objects
Module 4: Generative AI in Video
4.1 Generating Synthetic Video with GANs
4.2 AI-Driven Animation and Avatars
4.3 Ethical Use of Generative Content
4.4 Use Case: Auto-Generation of Product Explainer Videos using Avatars and Synthesized Narration
4.5 Case Study: Synthesia’s Solution Enabling Businesses to Create AI-Driven Training and Marketing Videos
4.6 Hands-On: Generate a Deepfake or AI Avatar using AKOOL, and Explore Face Alignment and Identity Swapping
Module 5: Enhancing Video with AI
5.1 Super-Resolution and Restoration
5.2 Real-Time Video Enhancement
5.3 Making Video More Inclusive
5.4 Use Case: Streaming Platforms using AI to Enhance Resolution and Reduce Latency for Mobile Users.
5.5 Case Study: DeOldify’s Impact in Reviving Historical Video Archives by Upscaling and Colorizing Black-and-White Footage.
5.6 Hands-On: Use AI4Video to Enhance a Sample Low-Resolution Black-and-White Video and Visualize Improvement
Module 6: Interactive and Immersive AI Video
6.1 AI in AR and Mixed Reality
6.2 Intelligent Video Editing
6.3 Viewer Engagement & Adaptation
6.4 Use Case: Live Sports Broadcasters using AR to Overlay Player Stats during Gameplay
6.5 Case Study: NFL and AWS Collaboration to Deliver Real-Time Performance Insights via Augmented Visuals.
6.6 Hands-On: Creating a Highlight Video from a Video Clip using Clipchamp
Module 7: AI in Video Surveillance and Compliance
7.1 Security and Monitoring Systems
7.2 Automated Content Moderation
7.3 Addressing Privacy and Ethics
7.4 Use Case: Automated Real-Time Access Control in Corporate Offices Using Facial Authentication.
7.5 Case Study: Amazon Go’s Cashier-less Stores Using Computer Vision for Security and Consumer Behavior Tracking
7.6 Hands-On: Implement Facial Detection and Access Control Simulation using OpenCV and a Basic Recognition Model
Module 8: Future of AI+ Video Practitioner™
8.1 Trends and Emerging Technologies
8.2 AI Applications by Industry
8.3 Careers and Professional Growth
Tools you will explore
TensorFlow
PyTorch
OpenCV
MediaPipe
Runway ML
Synthesia Studio
DeepFaceLab
Adobe Sensei
DaVinci Resolve Neural Engine
Runway
Pika Labs
Kaiber AI
DeepBrain AI Studio
NVIDIA Maxine SDK
Google Video AI API
FFmpeg Automation Tools
Unreal Engine with AI Plugins
Blender AI Add-ons
Stability Video Diffusion
Generative Video Editing Tools
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Supply Chain Practitioner™
Formerly known as AI+ Supply Chain™
Transforming Supply Chain Management
Comprehensive Learning: Covers logistics, operations, and supply chain digitization
Advanced Supply Strategies: Develop innovative supply strategies and workflows
Sector-Specific Solutions: Tailored sessions for real-world, sector-specific challenges
Lead AI Supply Efficiency: Prepares learners to lead in AI-led supply chain efficiency
Module 1: Introduction to Artificial Intelligence in Supply Chain
1.1 Overview of Artificial Intelligence in Supply Chain Management (SCM)
1.2 Transforming Supply Chains with AI
1.3 Ethical Implications of AI in Supply Chains
Module 2: Advanced AI Techniques for Supply Chain
2.1 Machine Learning in Supply Chain
2.2 Expert Systems in SCM
2.3 Integrating Images and Text in Supply Chain AI
Module 3: Generative AI in Supply Chain Management
3.1 The Origin of Generative AI
3.2 Generative AI in Revenue Management and Demand Forecasting
3.3 Transformer and LSTM Architectures in Generative AI
Module 4: Supply Chain Digitization
4.1 Introduction to Supply Chain Digitization
4.2 Supply Chain Integration and Push-Pull Strategies
4.3 Supply Chain Resiliency, Planning and Sustainability
Module 5: Intelligent Driven Supply Chain Management
5.1 Introduction to Smart SCM
5.2 Employing Smart SCM and Prompt Engineering
5.3 Future Trends of Smart SCM
Module 6: Industry Aspects of Advanced SCM
6.1 Introduction to Industrial SCM
6.2 Business Value from AI and Gen AI in Supply Chain
6.3 Risks and Challenges of Adopting AI and Gen AI in Industrial SCM
Module 7: Policies of Logistics Management in Supply Chain with AI
7.1 Role of Supply Chain Management in the Organization
7.2 Warehousing Strategy for Efficient Supply Chain Management
7.3 Technical Coverage of SCM with Multi-Dimensional Aspects
Module 8: Supply Chain Masterclass with AI Assistance
8.1 Supplier Selection and Relationship Management with AI
8.2 Mastering Advancements in SCM with Modern Artefacts
Optional Module: AI Agents for Supply Chain
What Are AI Agents
What Are AI Agents in Logistics and Supply Chain
Applications & Trends of AI Agents in Supply Chain
How Does an AI Agent Work
Core Characteristics of AI Agents
Key Advantages of AI Agents in Logistics and Supply Chain
Types of AI Agents
Tools you will explore
LeewayHertz (ZBrain)
C3.ai
Coupa (LLamasoft)
Zebra (Workcloud Demand Intelligence Suite)
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Supply Chain Practitioner™ eLearning
Formerly known as AI+ Supply Chain™
Transforming Supply Chain Management
Comprehensive Learning: Covers logistics, operations, and supply chain digitization
Advanced Supply Strategies: Develop innovative supply strategies and workflows
Sector-Specific Solutions: Tailored sessions for real-world, sector-specific challenges
Lead AI Supply Efficiency: Prepares learners to lead in AI-led supply chain efficiency
Module 1: Introduction to Artificial Intelligence in Supply Chain
1.1 Overview of Artificial Intelligence in Supply Chain Management (SCM)
1.2 Transforming Supply Chains with AI
1.3 Ethical Implications of AI in Supply Chains
Module 2: Advanced AI Techniques for Supply Chain
2.1 Machine Learning in Supply Chain
2.2 Expert Systems in SCM
2.3 Integrating Images and Text in Supply Chain AI
Module 3: Generative AI in Supply Chain Management
3.1 The Origin of Generative AI
3.2 Generative AI in Revenue Management and Demand Forecasting
3.3 Transformer and LSTM Architectures in Generative AI
Module 4: Supply Chain Digitization
4.1 Introduction to Supply Chain Digitization
4.2 Supply Chain Integration and Push-Pull Strategies
4.3 Supply Chain Resiliency, Planning and Sustainability
Module 5: Intelligent Driven Supply Chain Management
5.1 Introduction to Smart SCM
5.2 Employing Smart SCM and Prompt Engineering
5.3 Future Trends of Smart SCM
Module 6: Industry Aspects of Advanced SCM
6.1 Introduction to Industrial SCM
6.2 Business Value from AI and Gen AI in Supply Chain
6.3 Risks and Challenges of Adopting AI and Gen AI in Industrial SCM
Module 7: Policies of Logistics Management in Supply Chain with AI
7.1 Role of Supply Chain Management in the Organization
7.2 Warehousing Strategy for Efficient Supply Chain Management
7.3 Technical Coverage of SCM with Multi-Dimensional Aspects
Module 8: Supply Chain Masterclass with AI Assistance
8.1 Supplier Selection and Relationship Management with AI
8.2 Mastering Advancements in SCM with Modern Artefacts
Optional Module: AI Agents for Supply Chain
What Are AI Agents
What Are AI Agents in Logistics and Supply Chain
Applications & Trends of AI Agents in Supply Chain
How Does an AI Agent Work
Core Characteristics of AI Agents
Key Advantages of AI Agents in Logistics and Supply Chain
Types of AI Agents
Tools you will explore
LeewayHertz (ZBrain)
C3.ai
Coupa (LLamasoft)
Zebra (Workcloud Demand Intelligence Suite)
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag