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AI+ Robotics Practitioner™

AI+ Robotics Practitioner™
  • 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
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Certificate Code

AT-420

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  • 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. 1. What Are AI Agents
  2. 2. Key Capabilities of AI Agents in Robotics
  3. 3. Applications and Trends for AI Agents in Robotics
  4. 4. How Does an AI Agent Work
  5. 5. Core Characteristics of AI Agents
  6. 6. The Future of AI Agents in Robotics
  7. 7. Types of AI Agents

AI Tools Covered

OpenAI Gym
GreyOrange
Neurala
Dialogflow
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