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AI+ Developer Practitioner™
Get hands-on with the tools and technologies that power the AI ecosystem.
* Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
* Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
* Advanced Modules: Includes time series, model explainability, and cloud deployment
* Industry-Ready Skills: Prepares learners to design and deploy complex AI systems
Course Overview
* Course IntroductionPreview Module 1: Foundations of Artificial Intelligence
* 1.1 Introduction to AI Preview
* 1.2 Types of Artificial Intelligence Preview
* 1.3 Branches of Artificial Intelligence
* 1.4 Applications and Business Use Cases Module 2: Mathematical Concepts for AI
* 2.1 Linear Algebra Preview
* 2.2 Calculus Preview
* 2.3 Probability and Statistics Preview
* 2.4 Discrete Mathematics Module 3: Python for Developer
* 3.1 Python Fundamentals Preview
* 3.2 Python Libraries Module 4: Mastering Machine Learning
* 4.1 Introduction to Machine Learning
* 4.2 Supervised Machine Learning Algorithms
* 4.3 Unsupervised Machine Learning Algorithms
* 4.4 Model Evaluation and Selection Module 5: Deep Learning
* 5.1 Neural Networks
* 5.2 Improving Model Performance
* 5.3 Hands-on: Evaluating and Optimizing AI Models Module 6: Computer Vision
* 6.1 Image Processing Basics
* 6.2 Object Detection
* 6.3 Image Segmentation
* 6.4 Generative Adversarial Networks (GANs) Module 7: Natural Language Processing
* 7.1 Text Preprocessing and Representation
* 7.2 Text Classification
* 7.3 Named Entity Recognition (NER)
* 7.4 Question Answering (QA) Module 8: Reinforcement Learning
* 8.1 Introduction to Reinforcement Learning
* 8.2 Q-Learning and Deep Q-Networks (DQNs)
* 8.3 Policy Gradient Methods Module 9: Cloud Computing in AI Development
* 9.1 Cloud Computing for AI
* 9.2 Cloud-Based Machine Learning Services Module 10: Large Language Models
* 10.1 Understanding LLMs
* 10.2 Text Generation and Translation
* 10.3 Question Answering and Knowledge Extraction Module 11: Cutting-Edge AI Research
* 11.1 Neuro-Symbolic AI
* 11.2 Explainable AI (XAI)
* 11.3 Federated Learning
* 11.4 Meta-Learning and Few-Shot Learning Module 12: AI Communication and Documentation
* 12.1 Communicating AI Projects
* 12.2 Documenting AI Systems
* 12.3 Ethical Considerations Optional Module: AI Agents for Developers
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* GitHub Copilot
* Lobe
* H2O.ai
* Snorkel
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
€3.450
Klassikaal
max 12
5 dagen
AI+ Developer Practitioner™ eLearning
Get hands-on with the tools and technologies that power the AI ecosystem.
* Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
* Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
* Advanced Modules: Includes time series, model explainability, and cloud deployment
* Industry-Ready Skills: Prepares learners to design and deploy complex AI systems
Course Overview
* Course IntroductionPreview Module 1: Foundations of Artificial Intelligence
* 1.1 Introduction to AI Preview
* 1.2 Types of Artificial Intelligence Preview
* 1.3 Branches of Artificial Intelligence
* 1.4 Applications and Business Use Cases Module 2: Mathematical Concepts for AI
* 2.1 Linear Algebra Preview
* 2.2 Calculus Preview
* 2.3 Probability and Statistics Preview
* 2.4 Discrete Mathematics Module 3: Python for Developer
* 3.1 Python Fundamentals Preview
* 3.2 Python Libraries Module 4: Mastering Machine Learning
* 4.1 Introduction to Machine Learning
* 4.2 Supervised Machine Learning Algorithms
* 4.3 Unsupervised Machine Learning Algorithms
* 4.4 Model Evaluation and Selection Module 5: Deep Learning
* 5.1 Neural Networks
* 5.2 Improving Model Performance
* 5.3 Hands-on: Evaluating and Optimizing AI Models Module 6: Computer Vision
* 6.1 Image Processing Basics
* 6.2 Object Detection
* 6.3 Image Segmentation
* 6.4 Generative Adversarial Networks (GANs) Module 7: Natural Language Processing
* 7.1 Text Preprocessing and Representation
* 7.2 Text Classification
* 7.3 Named Entity Recognition (NER)
* 7.4 Question Answering (QA) Module 8: Reinforcement Learning
* 8.1 Introduction to Reinforcement Learning
* 8.2 Q-Learning and Deep Q-Networks (DQNs)
* 8.3 Policy Gradient Methods Module 9: Cloud Computing in AI Development
* 9.1 Cloud Computing for AI
* 9.2 Cloud-Based Machine Learning Services Module 10: Large Language Models
* 10.1 Understanding LLMs
* 10.2 Text Generation and Translation
* 10.3 Question Answering and Knowledge Extraction Module 11: Cutting-Edge AI Research
* 11.1 Neuro-Symbolic AI
* 11.2 Explainable AI (XAI)
* 11.3 Federated Learning
* 11.4 Meta-Learning and Few-Shot Learning Module 12: AI Communication and Documentation
* 12.1 Communicating AI Projects
* 12.2 Documenting AI Systems
* 12.3 Ethical Considerations Optional Module: AI Agents for Developers
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* GitHub Copilot
* Lobe
* H2O.ai
* Snorkel
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
€510
E-Learning
max 999
5 dagen
AI+ Architect Practitioner™
Formerly known as AI+ Architect™
Visualize Tomorrow: Neural Networks in Vision
* Deep AI Expertise: Covers neural networks, NLP, and computer vision frameworks
* Enterprise AI: Learn to design scalable AI systems for real-world impact
* Capstone Integration: Build, test, and deploy advanced AI architectures
* Industry Preparedness: Equips you for roles in high-demand AI design domains
Certification Overview
* Course Introduction Preview Module 1: Fundamentals of Neural Networks
* 1.1 Introduction to Neural Networks
* 1.2 Neural Network Architecture
* 1.3 Hands-on: Implement a Basic Neural Network Module 2: Neural Network Optimization
* 2.1 Hyperparameter Tuning
* 2.2 Optimization Algorithms
* 2.3 Regularization Techniques
* 2.4 Hands-on: Hyperparameter Tuning and Optimization Module 3: Neural Network Architectures for NLP
* 3.1 Key NLP Concepts
* 3.2 NLP-Specific Architectures
* 3.3 Hands-on: Implementing an NLP Model Module 4: Neural Network Architectures for Computer Vision
* 4.1 Key Computer Vision Concepts
* 4.2 Computer Vision-Specific Architectures
* 4.3 Hands-on: Building a Computer Vision Model Module 5: Model Evaluation and Performance Metrics
* 5.1 Model Evaluation Techniques
* 5.2 Improving Model Performance
* 5.3 Hands-on: Evaluating and Optimizing AI Models Module 6: AI Infrastructure and Deployment
* 6.1 Infrastructure for AI Development
* 6.2 Deployment Strategies
* 6.3 Hands-on: Deploying an AI Model Module 7: AI Ethics and Responsible AI Design
* 7.1 Ethical Considerations in AI
* 7.2 Best Practices for Responsible AI Design
* 7.3 Hands-on: Analyzing Ethical Considerations in AI Module 8: Generative AI Models
* 8.1 Overview of Generative AI Models
* 8.2 Generative AI Applications in Various Domains
* 8.3 Hands-on: Exploring Generative AI Models Module 9: Research-Based AI Design
* 9.1 AI Research Techniques
* 9.2 Cutting-Edge AI Design
* 9.3 Hands-on: Analyzing AI Research Papers Module 10: Capstone Project and Course Review
* 10.1 Capstone Project Presentation
* 10.2 Course Review and Future Directions
* 10.3 Hands-on: Capstone Project Development Optional Module: AI Agents for Architect
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* AutoGluon
* ChatGPT
* SonarCube
* Vertex AI
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
€3.450
Klassikaal
max 12
5 dagen
AI+ Architect Practitioner™ eLearning
Formerly known as AI+ Architect™
Visualize Tomorrow: Neural Networks in Vision
* Deep AI Expertise: Covers neural networks, NLP, and computer vision frameworks
* Enterprise AI: Learn to design scalable AI systems for real-world impact
* Capstone Integration: Build, test, and deploy advanced AI architectures
* Industry Preparedness: Equips you for roles in high-demand AI design domains
Certification Overview
* Course Introduction Preview Module 1: Fundamentals of Neural Networks
* 1.1 Introduction to Neural Networks
* 1.2 Neural Network Architecture
* 1.3 Hands-on: Implement a Basic Neural Network Module 2: Neural Network Optimization
* 2.1 Hyperparameter Tuning
* 2.2 Optimization Algorithms
* 2.3 Regularization Techniques
* 2.4 Hands-on: Hyperparameter Tuning and Optimization Module 3: Neural Network Architectures for NLP
* 3.1 Key NLP Concepts
* 3.2 NLP-Specific Architectures
* 3.3 Hands-on: Implementing an NLP Model Module 4: Neural Network Architectures for Computer Vision
* 4.1 Key Computer Vision Concepts
* 4.2 Computer Vision-Specific Architectures
* 4.3 Hands-on: Building a Computer Vision Model Module 5: Model Evaluation and Performance Metrics
* 5.1 Model Evaluation Techniques
* 5.2 Improving Model Performance
* 5.3 Hands-on: Evaluating and Optimizing AI Models Module 6: AI Infrastructure and Deployment
* 6.1 Infrastructure for AI Development
* 6.2 Deployment Strategies
* 6.3 Hands-on: Deploying an AI Model Module 7: AI Ethics and Responsible AI Design
* 7.1 Ethical Considerations in AI
* 7.2 Best Practices for Responsible AI Design
* 7.3 Hands-on: Analyzing Ethical Considerations in AI Module 8: Generative AI Models
* 8.1 Overview of Generative AI Models
* 8.2 Generative AI Applications in Various Domains
* 8.3 Hands-on: Exploring Generative AI Models Module 9: Research-Based AI Design
* 9.1 AI Research Techniques
* 9.2 Cutting-Edge AI Design
* 9.3 Hands-on: Analyzing AI Research Papers Module 10: Capstone Project and Course Review
* 10.1 Capstone Project Presentation
* 10.2 Course Review and Future Directions
* 10.3 Hands-on: Capstone Project Development Optional Module: AI Agents for Architect
* 1. Understanding AI Agents
* 2. Case Studies
* 3. Hands-On Practice with AI Agents Tools you will explore
* AutoGluon
* ChatGPT
* SonarCube
* Vertex AI
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
€510
E-Learning
max 999
5 dagen
AI+ Engineer Practitioner™
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
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 Tools you will explore
* 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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.450
Klassikaal
max 12
5 dagen
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
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 Tools you will explore
* 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.
Instructor-led OR Self-paced course + Official exam + Digital badge
€510
E-Learning
max 999
5 dagen
AI+ Quantum Practitioner™
Formerly known as AI+ Quantum™
Harness Quantum Power with AI
* AI + Quantum Integration: Explore Quantum Gates, Circuits, and AI applications
* Advanced Learnings: Includes Quantum Deep Learning and transformative AI methodologies
* Industry-Oriented: Real-world case studies and trend analysis
* Ethical Focus: Learn implications of quantum AI responsibly and efficiently
Module 1: Overview of Artificial Intelligence (AI) and Quantum Computing
* 1.1 Artificial Intelligence Refresher
* 1.2 Quantum Computing Refresher Module 2: Quantum Computing Gates, Circuits, and Algorithms
* 2.1 Quantum Gates and their Representation
* 2.2 Multi Qubit Systems and Multi Qubit Gates Module 3: Quantum Algorithms for AI
* 3.1 Core Quantum Algorithms
* 3.2 QFT and Variational Quantum Algorithms Module 4: Quantum Machine Learning
* 4.1 Algorithms for Regression and Classification
* 4.2 Algorithms for Dimensionality and Clustering Module 5: Quantum Deep Learning
* 5.1 Algorithms for Neural Networks – Part I
* 5.2 Algorithms for Neural Networks – Part II Module 6: Ethical Considerations
* 6.1 Ethics for Artificial Intelligence
* 6.2 Ethics for Quantum Computing Module 7: Trends and Outlook
* 7.1 Current Trends and Tools
* 7.2 Future Outlook and Investment Module 8: Use Cases & Case Studies
* 8.1 Quantum Use Cases
* 8.2 QML Case Studies Module 9: Workshop
* 9.1 Project – I: QSVM for Iris Dataset
* 9.2 Project – II: VQC/QNN on Iris Dataset
* 9.3 Bonus: IBM Quantum Computers Optional Module: AI Agents for Quantum
* 1. What Are AI Agents
* 2. Key Capabilities of AI Agents in Quantum Computing
* 3. Applications and Trends for AI Agents in Quantum Computing
* 4. How Does an AI Agent Work
* 5. Core Characteristics of AI Agents
* 6. Types of AI Agents Tools you will explore
* IBM Qiskit
* D-Wave Leap
* Google TensorFlow Quantum (TFQ)
* Amazon Braket
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
€3.450
Klassikaal
max 12
5 dagen
AI+ Quantum Practitioner™ eLearning
Formerly known as AI+ Quantum™
Harness Quantum Power with AI
* AI + Quantum Integration: Explore Quantum Gates, Circuits, and AI applications
* Advanced Learnings: Includes Quantum Deep Learning and transformative AI methodologies
* Industry-Oriented: Real-world case studies and trend analysis
* Ethical Focus: Learn implications of quantum AI responsibly and efficiently
Module 1: Overview of Artificial Intelligence (AI) and Quantum Computing
* 1.1 Artificial Intelligence Refresher
* 1.2 Quantum Computing Refresher Module 2: Quantum Computing Gates, Circuits, and Algorithms
* 2.1 Quantum Gates and their Representation
* 2.2 Multi Qubit Systems and Multi Qubit Gates Module 3: Quantum Algorithms for AI
* 3.1 Core Quantum Algorithms
* 3.2 QFT and Variational Quantum Algorithms Module 4: Quantum Machine Learning
* 4.1 Algorithms for Regression and Classification
* 4.2 Algorithms for Dimensionality and Clustering Module 5: Quantum Deep Learning
* 5.1 Algorithms for Neural Networks – Part I
* 5.2 Algorithms for Neural Networks – Part II Module 6: Ethical Considerations
* 6.1 Ethics for Artificial Intelligence
* 6.2 Ethics for Quantum Computing Module 7: Trends and Outlook
* 7.1 Current Trends and Tools
* 7.2 Future Outlook and Investment Module 8: Use Cases & Case Studies
* 8.1 Quantum Use Cases
* 8.2 QML Case Studies Module 9: Workshop
* 9.1 Project – I: QSVM for Iris Dataset
* 9.2 Project – II: VQC/QNN on Iris Dataset
* 9.3 Bonus: IBM Quantum Computers Optional Module: AI Agents for Quantum
* 1. What Are AI Agents
* 2. Key Capabilities of AI Agents in Quantum Computing
* 3. Applications and Trends for AI Agents in Quantum Computing
* 4. How Does an AI Agent Work
* 5. Core Characteristics of AI Agents
* 6. Types of AI Agents Tools you will explore
* IBM Qiskit
* D-Wave Leap
* Google TensorFlow Quantum (TFQ)
* Amazon Braket
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
€510
E-Learning
max 999
5 dagen
AI+ Robotics Practitioner™
Formerly known as AI+ Robotics™
Build the Future with Smart Automation
* AI-Driven Robotics: Apply AI in Deep Learning, Reinforcement Learning, and smart automation
* Real-World Systems: Work with autonomous systems and intelligent agents
* Ethics & Innovation: Learn industry-aligned practices and innovation strategies
* Hands-On Projects: Gain experience designing, optimising, and deploying robotics solutions
Module 1: Introduction to Robotics and Artificial Intelligence (AI)
* 1.1 Overview of Robotics: Introduction, History, Evolution, and Impact
* 1.2 Introduction to Artificial Intelligence (AI) in Robotics
* 1.3 Fundamentals of Machine Learning (ML) and Deep Learning
* 1.4 Role of Neural Networks in Robotics Module 2: Understanding AI and Robotics Mechanics
* 2.1 Components of AI Systems and Robotics
* 2.2 Deep Dive into Sensors, Actuators, and Control Systems
* 2.3 Exploring Machine Learning Algorithms in Robotics Module 3: Autonomous Systems and Intelligent Agents
* 3.1 Introduction to Autonomous Systems
* 3.2 Building Blocks of Intelligent Agents
* 3.3 Case Studies: Autonomous Vehicles and Industrial Robots
* 3.4 Key Platforms for Development: ROS (Robot Operating System) Module 4: AI and Robotics Development Frameworks
* 4.1 Python for Robotics and Machine Learning
* 4.2 TensorFlow and PyTorch for AI in Robotics
* 4.3 Introduction to Other Essential Frameworks Module 5: Deep Learning Algorithms in Robotics
* 5.1 Understanding Deep Learning: Neural Networks, CNNs
* 5.2 Robotic Vision Systems: Object Detection, Recognition
* 5.3 Hands-on Session: Training a CNN for Object Recognition
* 5.4 Use-case: Precision Manufacturing with Robotic Vision Module 6: Reinforcement Learning in Robotics
* 6.1 Basics of Reinforcement Learning (RL)
* 6.2 Implementing RL Algorithms for Robotics
* 6.3 Hands-on Session: Developing RL Models for Robots
* 6.4 Use-case: Optimizing Warehouse Operations with RL Module 7: Generative AI for Robotic Creativity
* 7.1 Exploring Generative AI: GANs and Applications
* 7.2 Creative Robots: Design, Creation, and Innovation
* 7.3 Hands-on Session: Generating Novel Designs for Robotics
* 7.4 Use-case: Custom Manufacturing with AI Module 8: Natural Language Processing (NLP) for Human-Robot Interaction
* 8.1 Introduction to NLP for Robotics
* 8.2 Voice-Activated Control Systems
* 8.3 Hands-on Session: Creating a Voice-command Robot Interface
* 8.4 Case-Study: Assistive Robots in Healthcare Module 9: Practical Activities and Use-Cases
* 9.1 Hands-on Session-1: Building AI Models for Object Recognition using Python Programming
* 9.2 Hands-on Session-2: Path Planning, Obstacle Avoidance, and Localization Implementation using Python Programming
* 9.3 Hands-on Session-3: PID Controller Implementation using Python programming
* 9.4 Use-cases: Precision Agriculture, Automated Assembly Lines Module 10: Emerging Technologies and Innovation in Robotics
* 10.1 Integration of Blockchain and Robotics
* 10.2 Quantum Computing and Its Potential Module 11: Exploring AI with Robotic Process Automation
* 11.1 Understanding Robotic Process Automation and its use cases
* 11.2 Popular RPA Tools and Their Features
* 11.3 Integrating AI with RPA Module 12: AI Ethics, Safety, and Policy
* 12.1 Ethical Considerations in AI and Robotics
* 12.2 Safety Standards for AI-Driven Robotics
* 12.3 Discussion: Navigating AI Policies and Regulations Module 13: Innovations and Future Trends in AI and Robotics
* 13.1 Latest Innovations in Robotics and AI
* 13.2 Future of Work and Society: Impact of AI and Robotics Optional Module: AI Agents for Robotics
* 1. What Are AI Agents
* 2. Key Capabilities of AI Agents in Robotics
* 3. Applications and Trends for AI Agents in Robotics
* 4. How Does an AI Agent Work
* 5. Core Characteristics of AI Agents
* 6. The Future of AI Agents in Robotics
* 7. Types of AI Agents Tools you will explore
* OpenAI Gym
* GreyOrange
* Neurala
* Dialogflow
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
€3.450
Klassikaal
max 12
5 dagen
AI+ Robotics Practitioner™ eLearning
Formerly known as AI+ Robotics™
Build the Future with Smart Automation
* AI-Driven Robotics: Apply AI in Deep Learning, Reinforcement Learning, and smart automation
* Real-World Systems: Work with autonomous systems and intelligent agents
* Ethics & Innovation: Learn industry-aligned practices and innovation strategies
* Hands-On Projects: Gain experience designing, optimising, and deploying robotics solutions
Module 1: Introduction to Robotics and Artificial Intelligence (AI)
* 1.1 Overview of Robotics: Introduction, History, Evolution, and Impact
* 1.2 Introduction to Artificial Intelligence (AI) in Robotics
* 1.3 Fundamentals of Machine Learning (ML) and Deep Learning
* 1.4 Role of Neural Networks in Robotics Module 2: Understanding AI and Robotics Mechanics
* 2.1 Components of AI Systems and Robotics
* 2.2 Deep Dive into Sensors, Actuators, and Control Systems
* 2.3 Exploring Machine Learning Algorithms in Robotics Module 3: Autonomous Systems and Intelligent Agents
* 3.1 Introduction to Autonomous Systems
* 3.2 Building Blocks of Intelligent Agents
* 3.3 Case Studies: Autonomous Vehicles and Industrial Robots
* 3.4 Key Platforms for Development: ROS (Robot Operating System) Module 4: AI and Robotics Development Frameworks
* 4.1 Python for Robotics and Machine Learning
* 4.2 TensorFlow and PyTorch for AI in Robotics
* 4.3 Introduction to Other Essential Frameworks Module 5: Deep Learning Algorithms in Robotics
* 5.1 Understanding Deep Learning: Neural Networks, CNNs
* 5.2 Robotic Vision Systems: Object Detection, Recognition
* 5.3 Hands-on Session: Training a CNN for Object Recognition
* 5.4 Use-case: Precision Manufacturing with Robotic Vision Module 6: Reinforcement Learning in Robotics
* 6.1 Basics of Reinforcement Learning (RL)
* 6.2 Implementing RL Algorithms for Robotics
* 6.3 Hands-on Session: Developing RL Models for Robots
* 6.4 Use-case: Optimizing Warehouse Operations with RL Module 7: Generative AI for Robotic Creativity
* 7.1 Exploring Generative AI: GANs and Applications
* 7.2 Creative Robots: Design, Creation, and Innovation
* 7.3 Hands-on Session: Generating Novel Designs for Robotics
* 7.4 Use-case: Custom Manufacturing with AI Module 8: Natural Language Processing (NLP) for Human-Robot Interaction
* 8.1 Introduction to NLP for Robotics
* 8.2 Voice-Activated Control Systems
* 8.3 Hands-on Session: Creating a Voice-command Robot Interface
* 8.4 Case-Study: Assistive Robots in Healthcare Module 9: Practical Activities and Use-Cases
* 9.1 Hands-on Session-1: Building AI Models for Object Recognition using Python Programming
* 9.2 Hands-on Session-2: Path Planning, Obstacle Avoidance, and Localization Implementation using Python Programming
* 9.3 Hands-on Session-3: PID Controller Implementation using Python programming
* 9.4 Use-cases: Precision Agriculture, Automated Assembly Lines Module 10: Emerging Technologies and Innovation in Robotics
* 10.1 Integration of Blockchain and Robotics
* 10.2 Quantum Computing and Its Potential Module 11: Exploring AI with Robotic Process Automation
* 11.1 Understanding Robotic Process Automation and its use cases
* 11.2 Popular RPA Tools and Their Features
* 11.3 Integrating AI with RPA Module 12: AI Ethics, Safety, and Policy
* 12.1 Ethical Considerations in AI and Robotics
* 12.2 Safety Standards for AI-Driven Robotics
* 12.3 Discussion: Navigating AI Policies and Regulations Module 13: Innovations and Future Trends in AI and Robotics
* 13.1 Latest Innovations in Robotics and AI
* 13.2 Future of Work and Society: Impact of AI and Robotics Optional Module: AI Agents for Robotics
* 1. What Are AI Agents
* 2. Key Capabilities of AI Agents in Robotics
* 3. Applications and Trends for AI Agents in Robotics
* 4. How Does an AI Agent Work
* 5. Core Characteristics of AI Agents
* 6. The Future of AI Agents in Robotics
* 7. Types of AI Agents Tools you will explore
* OpenAI Gym
* GreyOrange
* Neurala
* Dialogflow
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
€510
E-Learning
max 999
5 dagen