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AI+ Real Estate Practitioner™
Formerly known as AI+ Real Estate™<br><br>AI in Real Estate Practitioner: Pioneering the Future of Property Innovation
Foundational Insights: Understand the core AI technologies shaping real estate, from automated valuation models to predictive analytics and smart home systems.
Advanced Applications: Master AI tools for property valuation, dynamic market forecasting, fraud detection, targeted marketing, enhancing decision-making and operational efficiency.
Specialized Expertise: Deepen your knowledge of AI in investment strategies, risk management, energy optimization, and compliance to unlock new levels of business performance.
Capstone Project: Create AI-driven solutions for real-world real estate challenges—automating pricing, optimizing property investments, and securing transactions.
Module 1: Introduction to AI & Machine Learning in Real Estate
1.1 Introduction to AI
1.2 Types of Machine Learning (ML) in Real Estate
1.3 Challenges & Limitations of AI
1.4 Use Cases
1.5 Case Study
1.6 Hands-on
Module 2: AI in Property Valuation & Price Prediction
2.1 How AI Estimates Property Values
2.2 Comparative Market Analysis (CMA) with AI
2.3 AI for Future Market Trend Forecasting
2.4 Use Cases
2.5 Case Study
2.6 Hands-on
Module 3: AI in Marketing & Lead Generation
3.1 AI for Real Estate Marketing & Personalization
3.2 AI Chatbots & Virtual Assistants
3.3 AI in Social Media & SEO
3.4 Use Cases
3.5 Case Study
3.6 Hands-on
Module 4: AI for Fraud Detection & Risk Management
4.1 AI for Detecting Real Estate Fraud
4.2 AI for Loan & Mortgage Risk Assessment
4.3 AI for Anti-Money Laundering (AML) in Real Estate
4.4 Use Cases
4.5 Case Study
4.6 Hands-on
Module 5: AI in Smart Homes & Property Automation
5.1 AI-Powered Smart Homes & IoT
5.2 AI for Energy Efficiency & Sustainability
5.3 AI-Enhanced Security & Surveillance
5.4 Use Cases
5.5 Case Study
5.6 Hands-on
Module 6: AI in Compliance & Ethics
6.1 AI’s Role in Fair Lending & Bias Detection
6.2 AI-Powered Legal Document Verification
6.3 Regulatory Challenges & Ethical Concerns
6.4 Use Cases
6.5 Case Study
6.6 Hands-on
Module 7: AI for Business Strategy & Decision-Making
7.1 AI in Real Estate Investment & Site Selection
7.2 AI-Driven Risk Management & Predictive Maintenance
7.3 AI in Real Estate Portfolio Optimization
7.4 Use Cases
7.5 Case Study
7.6 Hands-on
Module 8: AI Strategy & Capstone Project
8.1 Real-World Case Study: “End-to-End AI Implementation in Real Estate”
8.2 Final Project: AI Strategy Implementation
Tools you will explore
TensorFlow
Keras
Hadoop
Power BI
Python
Tableau
Matplotlib
SQL
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+ Real Estate Practitioner™ eLearning
Formerly known as AI+ Real Estate™<br><br>AI in Real Estate Practitioner: Pioneering the Future of Property Innovation
Foundational Insights: Understand the core AI technologies shaping real estate, from automated valuation models to predictive analytics and smart home systems.
Advanced Applications: Master AI tools for property valuation, dynamic market forecasting, fraud detection, targeted marketing, enhancing decision-making and operational efficiency.
Specialized Expertise: Deepen your knowledge of AI in investment strategies, risk management, energy optimization, and compliance to unlock new levels of business performance.
Capstone Project: Create AI-driven solutions for real-world real estate challenges—automating pricing, optimizing property investments, and securing transactions.
Module 1: Introduction to AI & Machine Learning in Real Estate
1.1 Introduction to AI
1.2 Types of Machine Learning (ML) in Real Estate
1.3 Challenges & Limitations of AI
1.4 Use Cases
1.5 Case Study
1.6 Hands-on
Module 2: AI in Property Valuation & Price Prediction
2.1 How AI Estimates Property Values
2.2 Comparative Market Analysis (CMA) with AI
2.3 AI for Future Market Trend Forecasting
2.4 Use Cases
2.5 Case Study
2.6 Hands-on
Module 3: AI in Marketing & Lead Generation
3.1 AI for Real Estate Marketing & Personalization
3.2 AI Chatbots & Virtual Assistants
3.3 AI in Social Media & SEO
3.4 Use Cases
3.5 Case Study
3.6 Hands-on
Module 4: AI for Fraud Detection & Risk Management
4.1 AI for Detecting Real Estate Fraud
4.2 AI for Loan & Mortgage Risk Assessment
4.3 AI for Anti-Money Laundering (AML) in Real Estate
4.4 Use Cases
4.5 Case Study
4.6 Hands-on
Module 5: AI in Smart Homes & Property Automation
5.1 AI-Powered Smart Homes & IoT
5.2 AI for Energy Efficiency & Sustainability
5.3 AI-Enhanced Security & Surveillance
5.4 Use Cases
5.5 Case Study
5.6 Hands-on
Module 6: AI in Compliance & Ethics
6.1 AI’s Role in Fair Lending & Bias Detection
6.2 AI-Powered Legal Document Verification
6.3 Regulatory Challenges & Ethical Concerns
6.4 Use Cases
6.5 Case Study
6.6 Hands-on
Module 7: AI for Business Strategy & Decision-Making
7.1 AI in Real Estate Investment & Site Selection
7.2 AI-Driven Risk Management & Predictive Maintenance
7.3 AI in Real Estate Portfolio Optimization
7.4 Use Cases
7.5 Case Study
7.6 Hands-on
Module 8: AI Strategy & Capstone Project
8.1 Real-World Case Study: “End-to-End AI Implementation in Real Estate”
8.2 Final Project: AI Strategy Implementation
Tools you will explore
TensorFlow
Keras
Hadoop
Power BI
Python
Tableau
Matplotlib
SQL
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+ Gaming™
Discover how AI transforms game design, player engagement, and virtual environments. Build real-world gaming projects using cutting-edge AI technologies.
Comprehensive Skill Development
Master AI-driven game design, adaptive storytelling, and intelligent NPC development to create immersive, data-enhanced gaming experiences.
Industry Recognition
Earn a globally recognized certification that validates your expertise in integrating artificial intelligence within modern gaming environments.
Hands-On Learning
Work on real-world gaming projects, from AI-based character behavior modeling to predictive player analytics, enhancing creativity and technical precision.
Career Advancement
Unlock career opportunities in game development, AI simulation design, virtual production, and interactive entertainment industries.
Future-Ready Expertise
Stay at the forefront of gaming innovation with cutting-edge knowledge 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+ Gaming™ eLearning
Discover how AI transforms game design, player engagement, and virtual environments. Build real-world gaming projects using cutting-edge AI technologies.
Comprehensive Skill Development
Master AI-driven game design, adaptive storytelling, and intelligent NPC development to create immersive, data-enhanced gaming experiences.
Industry Recognition
Earn a globally recognized certification that validates your expertise in integrating artificial intelligence within modern gaming environments.
Hands-On Learning
Work on real-world gaming projects, from AI-based character behavior modeling to predictive player analytics, enhancing creativity and technical precision.
Career Advancement
Unlock career opportunities in game development, AI simulation design, virtual production, and interactive entertainment industries.
Future-Ready Expertise
Stay at the forefront of gaming innovation with cutting-edge knowledge 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™
Empower creators with AI + Game Design Agent™ to craft intelligent, dynamic, and immersive gaming experiences.
Comprehensive Skill Development
Master AI-driven game design by integrating procedural generation, adaptive storytelling, and intelligent NPC behavior to create immersive, dynamic gaming experiences.
Industry Recognition
Earn a globally recognized certification that highlights your expertise in blending artificial intelligence with creative game development.
Hands-On Learning
Practice with real-world projects involving AI-based level design, character behavior modeling, and player experience optimization to sharpen your practical game design skills.
Career Advancement
Explore opportunities in AI game development, interactive design, and simulation engineering across gaming studios, tech companies, and entertainment platforms.
Future-Ready Expertise
Stay ahead in the next era of gaming innovation with deep knowledge of generative AI, autonomous systems, and adaptive gameplay design.
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™ eLearning
Empower creators with AI + Game Design Agent™ to craft intelligent, dynamic, and immersive gaming experiences.
Comprehensive Skill Development
Master AI-driven game design by integrating procedural generation, adaptive storytelling, and intelligent NPC behavior to create immersive, dynamic gaming experiences.
Industry Recognition
Earn a globally recognized certification that highlights your expertise in blending artificial intelligence with creative game development.
Hands-On Learning
Practice with real-world projects involving AI-based level design, character behavior modeling, and player experience optimization to sharpen your practical game design skills.
Career Advancement
Explore opportunities in AI game development, interactive design, and simulation engineering across gaming studios, tech companies, and entertainment platforms.
Future-Ready Expertise
Stay ahead in the next era of gaming innovation with deep knowledge of generative AI, autonomous systems, and adaptive gameplay design.
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™<br> <br> 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™<br> <br> 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™
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
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™ 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
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