CBSE Class 11th AI (Artificial Intelligence) | 12. AI-Based Activities (like Emoji Generator, Face Detection, etc.) by Abraham | Learn Smarter
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12. AI-Based Activities (like Emoji Generator, Face Detection, etc.)

Artificial Intelligence (AI) encompasses practical applications that facilitate understanding of its principles through engaging activities. This chapter focuses on hands-on projects like Emoji Generators, Face Detection, and Pose Estimation, illustrating key AI concepts such as classification and object detection. Through interactive exercises, students are introduced to AI's real-world applications, providing a bridge between theoretical knowledge and practical implementation.

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Sections

  • 12

    Ai-Based Activities (Like Emoji Generator, Face Detection, Etc.)

    This section explores hands-on AI projects like Emoji Generators and Face Detection, enabling students to understand AI applications practically.

    Learning Practice

  • 12.1

    Emoji Generator

    The Emoji Generator is an AI application that translates facial expressions into emojis using trained models.

    Learning Practice

  • 12.1.1

    What Is It?

    This section introduces the concept of the Emoji Generator, an AI application that maps human emotions to emoji representations using an image classification model.

    Learning Practice

  • 12.1.2

    Concepts Involved

    This section delves into the fundamental concepts underlying various AI applications like Emoji Generators, Face Detection, and Pose Estimation.

    Learning Practice

  • 12.1.3

    Steps To Build

    This section outlines the essential steps to build an AI-based Emoji Generator using Teachable Machine.

    Learning Practice

  • 12.1.4

    Educational Outcomes

    This section outlines the educational outcomes related to AI-based activities, emphasizing the importance of understanding AI concepts and applications.

    Learning Practice

  • 12.2

    Face Detection

    Face detection identifies human faces in images or videos but does not recognize individuals.

    Learning Practice

  • 12.2.2

    Concepts Involved

    This section covers key concepts relevant to Face Detection, including object detection and the use of AI tools such as OpenCV.

    Learning Practice

  • 12.2.3

    Steps To Build (Python-Based)

    This section outlines the steps needed to implement Face Detection using OpenCV in Python.

    Learning Practice

  • 12.2.4

    Educational Outcomes

    This section describes the educational outcomes from hands-on AI activities, outlining essential learning concepts associated with AI applications.

    Learning Practice

  • 12.3

    Pose Estimation

    Pose estimation involves detecting human posture and key body points from images or video using AI technologies.

    Learning Practice

  • 12.3.2

    Concepts Involved

    This section discusses the concepts involved in AI applications like Emoji Generators and Pose Estimation.

    Learning Practice

  • 12.3.3

    Tools

    This section introduces AI tools like TensorFlow.js and MediaPipe used for pose estimation, highlighting their applications and implementation steps.

    Learning Practice

  • 12.3.4

    Steps (Posenet Js Example)

    This section describes the steps to implement Pose Estimation using PoseNet with TensorFlow.js.

    Learning Practice

  • 12.3.5

    Applications

    This section introduces various applications of AI, including Emoji Generators, Face Detection, and Pose Estimation, emphasizing their educational value.

    Learning Practice

  • 12.4

    Teachable Machine Experiments

    Teachable Machine is a user-friendly tool by Google that allows students to create custom machine learning models without coding, making AI accessible for beginners.

    Learning Practice

  • 12.4.1

    What Is Teachable Machine?

    Teachable Machine is a user-friendly browser tool that enables students to create custom machine learning models for image, sound, and pose without needing programming skills.

    Learning Practice

  • 12.4.2

    Experiments To Try

    This section outlines various AI experiments using tools like Teachable Machine to engage students in machine learning model creation.

    Learning Practice

  • 12.4.3

    Why Use It?

    Teachable Machine serves as a user-friendly platform for training AI models, encouraging practical engagement with machine learning concepts.

    Learning Practice

  • 12.5

    Ai With Scratch And Blockly

    This section introduces using visual programming tools like Scratch and Blockly to help beginners understand AI concepts.

    Learning Practice

  • 12.5.1

    Why Use Visual Tools?

    Visual tools like Scratch and Blockly simplify AI learning for beginners, making complex concepts more accessible.

    Learning Practice

  • 12.5.2

    Example Activities

    This section explores example activities using AI tools to engage students in hands-on learning.

    Learning Practice

  • 12.6

    Ethical Considerations

    This section discusses the ethical considerations that arise in the development and use of AI-based applications, focusing on bias, data privacy, and overfitting.

    Learning Practice

  • 12.6.1

    Points To Reflect On

    This section explores the ethical implications of AI applications in education, emphasizing bias, privacy, and overfitting.

    Learning Practice

  • 12.7

    Chapter Summary

    This section reviews the major AI applications discussed in the chapter, summarizing key concepts like emoji generation, face detection, pose estimation, and ethical considerations.

    Learning Practice

  • 12.7.1

    Key Concept Description

    This section covers key AI applications such as Emoji Generators, Face Detection, and Pose Estimation, discussing their functionalities and educational outcomes.

    Learning Practice

  • 12.8

    Key Takeaways

    In this section, we summarize the essential concepts of AI-based activities, emphasizing the practical applications and understanding of AI for students.

    Learning Practice

References

Chapter_12_AI.pdf

Class Notes

Memorization

What we have learnt

  • You don’t need to be a code...
  • Activities make abstract AI...
  • With simple tools, students...

Revision Tests