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Build the Future with Smart Automation

The AI+ Robotics certification program offers a transformative journey into the dynamic intersection of Artificial Intelligence (AI) and Robotics. From foundational concepts to advanced Deep Learning algorithms and Reinforcement Learning, the immersive experience is tailored for Robotics applications. Each module provides a well-rounded understanding, exploring autonomous systems, intelligent agents, and generative AI. By the program's end, acquire both robust theoretical knowledge and practical expertise, empowering you to lead innovation in the ever-evolving AI and Robotics landscape.

Prerequisites

  • Familiarity with basic concepts of Artificial Intelligence (AI), without the need for technical expertise.

 

  • Openness to generate innovative ideas and concepts, leveraging AI tools effectively in the process.

 

  • Ability to analyze information critically and evaluate the implications of AI and Robotics technologies.

 

  • Readiness to engage in problem-solving activities and apply AI techniques to real-world scenario

Exam Details

Modules

13

Examination

1

Minutes

90

Passing score

70%

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

What you will learn

Algorithm Development and Implementation

Developing the ability to implement deep learning and reinforcement learning algorithms specifically tailored for robotics, equipping learners with the skills to create intelligent and adaptive robotic behaviors.

Human-Robot Interaction and Communication

Gaining expertise in Natural Language Processing (NLP) for facilitating effective human-robot interaction, enhancing the ability of robots to understand and respond to human commands and communications

Generative AI for Creative Applications

Learning to apply generative AI techniques for enhancing robotic creativity, allowing robots to generate novel solutions and approaches in various tasks and problem-solving scenarios.

Practical Application and Use-Case Implementation

Developing hands-on experience through practical activities and real-world use-cases, which reinforces theoretical knowledge and provides learners with the skills to apply their learning to actual robotic projects and challenges.

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