AI Data & Robotics
AI+ Robotics
This certification program covers AI and Robotics fundamentals, Deep Learning, Reinforcement Learning, and autonomous systems. It includes hands-on activities, real-world case studies, and ethical considerations. Stay updated on emerging trends, gaining both theoretical knowledge and practical expertise to lead innovation in AI and Robotics.
Targeted Audience
AI Robotics Integration Expert
Robotics Engineer With AI Expertise
AI Robotics System Developer
AI Robotics Integration Expert
Modules | 13
Examination | 1
Passing Score | 70%
COURSE AGENDA
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: Data Sources and Types
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 9: Advance Machine Learning ]
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 RPAx.
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.