AI+ Robotics Practitioner™
- From 505 EUR + VAT
- Self-paced online Start anytime
- Duration 40 hours of content, self-paced
- Language English
- Topic AI Data & Robotics
Overview
What the course covers
The course, the materials and the exam are in English. The description below is the original AI CERTs® text.
- 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
Prerequisites
Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
Audience
Who is it for
Robotics Engineers Enhance robotic system design and functionality using AI for automation and control.
Mechanical Engineers: Integrate AI to optimize robotics systems and improve performance in manufacturing and production.
AI Specialists: Apply AI techniques to enhance the intelligence and autonomy of robotic systems.
IT Specialists & System Integrators: Implement AI-powered solutions to improve robotics infrastructure and communication systems.
Students & New Graduates: Build essential skills in AI and robotics to succeed in an emerging field with endless growth potential.
In the price
What the price includes
- Self-paced online course with the full learning material
- The official AI CERTs® exam
- Digital badge after passing the exam
Course content
Syllabus
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
Exam
About the exam
- 50 Questions
- 90 min Time limit
- 70% Pass mark
- Online proctored exam
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