CBSE Class 10th AI (Artificial Intelleigence) | 14. Revisiting AI Project Cycle, Data by Abraham | Learn Smarter
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14. Revisiting AI Project Cycle, Data

The chapter focuses on the importance of Data Collection and Data Access within the AI Project Cycle, emphasizing how these stages serve as the foundation for developing effective AI models. It outlines different types and sources of data, tools for data collection, alongside methods for data access, while also stressing the legal and ethical considerations that must be adhered to when handling data.

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Sections

  • 14

    Revisiting Ai Project Cycle, Data Collection, Data Access

    This section revisits the AI Project Cycle, focusing on the critical stages of data collection and data access, which are essential for developing effective AI models.

    Learning Practice

  • 14.1

    The Ai Project Cycle – A Quick Recap

    The section provides a concise overview of the AI Project Cycle, highlighting the vital stages of problem scoping, data collection, data exploration, modeling, and evaluation, with a focus on data collection and access.

    Learning Practice

  • 14.2

    Data Collection

    Data Collection is the crucial process of gathering information for AI models, impacting their ability to learn and predict accurately.

    Learning Practice

  • 14.2.1

    What Is Data Collection?

    Data Collection is a crucial process in the AI Project Cycle that involves gathering information to train AI models effectively.

    Learning Practice

  • 14.2.2

    Why Is Data Collection Important?

    Data collection is vital for training AI models as it directly impacts the accuracy of predictions and the performance of AI systems.

    Learning Practice

  • 14.2.3

    Types Of Data

    This section introduces various types of data relevant to AI and highlights their importance in data collection for effective model training.

    Learning Practice

  • 14.2.4

    Sources Of Data

    This section outlines the various types and sources of data used for training AI models, emphasizing the importance of data quality and legal considerations.

    Learning Practice

  • 14.2.5

    Data Collection Tools And Platforms

    This section focuses on the various tools and platforms available for data collection in AI projects.

    Learning Practice

  • 14.3

    Data Access

    Data Access focuses on methods to access, manage, and store data securely for AI model training.

    Learning Practice

  • 14.3.1

    Methods Of Data Access

    This section discusses various methods of accessing data, including local and cloud storage, databases, and APIs, along with the importance of legal compliance.

    Learning Practice

  • 14.4

    Legal And Ethical Considerations

    The section covers the legal and ethical responsibilities involved in handling data in AI projects.

    Learning Practice

  • 14.4.1

    Key Principles

    The key principles highlight the essential legal and ethical considerations in data management for AI projects.

    Learning Practice

  • 14.4.2

    Legal Frameworks To Know

    This section highlights key legal frameworks governing data use in AI projects, emphasizing the need for compliance with data protection regulations.

    Learning Practice

  • 14.5

    Quality Of Data: Garbage In, Garbage Out

    The quality of data directly affects the accuracy of AI models; bad data leads to poor predictions.

    Learning Practice

  • 14.5.1

    Good Data Characteristics

    Good data characteristics are essential for training effective AI models, ensuring accuracy and relevance.

    Learning Practice

  • 14.6

    Hands-On Activity Ideas (Optional For Teachers/students)

    This section presents hands-on activity ideas to engage students in data collection and analysis.

    Learning Practice

References

Chapter_14_Revis.pdf

Class Notes

Memorization

What we have learnt

  • Data Collection is crucial ...
  • Quality data leads to bette...
  • Legal and ethical considera...

Final Test

Revision Tests