AI+ Robotics™
AT-420
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
Why This Certification Matters
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
At a Glance: Course + Exam Overview
Program Name
AI+ Robotics™
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Prerequisites
Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Delivery
Online labs, projects, case studies
Outcome
Industry-recognized credential + hands-on experience
Job Roles & Industry Outlook
AI Specialist
Apply AI technologies to solve business problems and drive innovation across industries.
Business Analyst
Analyze business processes and leverage AI to optimize operations and decision-making.
Data Analyst
Transform data into actionable insights using AI-powered analytics tools.
Technology Consultant
Guide organizations in adopting AI solutions aligned with their strategic goals.
Innovation Manager
Lead AI-driven innovation initiatives to enhance organizational capabilities.
Industry Growth: AI+ Robotics™
- Demand for Certified AI & Robotics Professionals: Organizations are seeking certified professionals who can integrate AI into robotics to optimize processes, enhance automation, and improve operational efficiency.
- Risks of Mismanaging AI & Robotics: Mismanagement of robotic systems and AI technologies can lead to operational inefficiencies and safety risks.
- Role of Certification in Robotics Strategy: Certified professionals are key in developing robotics strategies that maximize performance, safety, and compliance with industry regulations.
- Career Advantage & Leadership Opportunities: As robotics and AI continue to reshape industries, this certification offers professionals a distinct advantage, positioning them for leadership roles.
Who Should Enroll?
- 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.
What You'll Learn
- 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
- 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
- 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)
- 4.1 Python for Robotics and Machine Learning
- 4.2 TensorFlow and PyTorch for AI in Robotics
- 4.3 Introduction to Other Essential Frameworks
- 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
- 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
- 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
- 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
- 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
- 10.1 Integration of Blockchain and Robotics
- 10.2 Quantum Computing and Its Potential
- 11.1 Understanding Robotic Process Automation and its use cases
- 11.2 Popular RPA Tools and Their Features
- 11.3 Integrating AI with RPA
- 12.1 Ethical Considerations in AI and Robotics
- 12.2 Safety Standards for AI-Driven Robotics
- 12.3 Discussion: Navigating AI Policies and Regulations
- 13.1 Latest Innovations in Robotics and AI
- 13.2 Future of Work and Society: Impact of AI and 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'll Explore
OpenAI Gym
GreyOrange
Neurala
Dialogflow
Prerequisites
- Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Introduction to Robotics and Artificial Intelligence (AI) - 7%
- Module 2: Understanding AI and Robotics Mechanics - 7%
- Module 3: Autonomous Systems and Intelligent Agents - 7%
- Module 4: AI and Robotics Development Frameworks - 7%
- Module 5: Deep Learning Algorithms in Robotics - 7%
- Module 6: Reinforcement Learning in Robotics - 7%
- Module 7: Generative AI for Robotic Creativity - 7%
- Module 8: Natural Language Processing (NLP) for Human-Robot Interaction - 7%
- Module 9: Practical Activities and Use-Cases - 7%
- Module 10: Emerging Technologies and Innovation in Robotics - 7%
- Module 11: Exploring AI with Robotic Process Automation - 7%
- Module 12: AI Ethics, Safety, and Policy - 7%
- Module 13: Innovations and Future Trends in AI and Robotics - 8%
- Optional Module: AI Agents for Robotics - 8%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.