Why This Certification Matters

  • 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.

At a Glance: Course + Exam Overview

Program Name
AI+ Robotics Practitioner™
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
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
Who Should Enroll?

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

  • 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

  • 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 Master

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%

Format

50 questions

Delivery Method

online proctored exam

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
Instructor-Led (Live Virtual/Classroom)
  • 5 days (live or virtual) Self-Paced: 40 hours of content
  • Real-time Q&A, peer collaboration, and hands-on labs
  • Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
  • 40 hours of content
  • Learn anywhere, anytime, with modular quizzes to track progress

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