CBSE Class 10th AI (Artificial Intelleigence) | 5. Data Acquisition by Abraham | Learn Smarter
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5. Data Acquisition

Data Acquisition is vital for successful AI systems, forming the foundation upon which quality models are built. The process involves gathering data from various structured, unstructured, and semi-structured sources using techniques like surveys, sensors, APIs, and web scraping. Understanding the types of data, the significance of both primary and secondary sources, and addressing challenges such as legal, ethical, and quality issues are critical for effective data acquisition practices.

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Sections

  • 5

    Data Acquisition

    Data Acquisition is the essential process of collecting and measuring data from varied sources in artificial intelligence, laying the groundwork for training and decision-making.

    Learning Practice

  • 5.1

    What Is Data Acquisition?

    Data Acquisition is the systematic process of collecting and measuring data from various sources to support AI analysis and decision-making.

    Learning Practice

  • 5.2

    Types Of Data

    This section discusses different types of data relevant in AI, namely structured, unstructured, and semi-structured data.

    Learning Practice

  • 5.2.a

    Structured Data

    Structured data is organized information formatted in rows and columns, making it easy to process and analyze.

    Learning Practice

  • 5.2.b

    Unstructured Data

    Unstructured data is information that does not follow a predefined format, requiring additional preprocessing before it can be analyzed or used in AI models.

    Learning Practice

  • 5.2.c

    Semi-Structured Data

    Semi-structured data is a blend of structured and unstructured data, allowing separation of elements via tags or markers.

    Learning Practice

  • 5.3

    Sources Of Data

    This section discusses the different sources from which data can be acquired, focusing on primary and secondary sources.

    Learning Practice

  • 5.3.a

    Primary Sources

    Primary sources are firsthand data collected for specific purposes and are crucial for accurate AI analysis and training.

    Learning Practice

  • 5.3.b

    Secondary Sources

    Secondary sources are pre-existing data that can be utilized for analysis, requiring verification for accuracy.

    Learning Practice

  • 5.4

    Data Acquisition Tools And Technologies

    This section discusses various tools and technologies used to acquire data for AI, emphasizing the importance of effective data collection methods in real-world applications.

    Learning Practice

  • 5.4.a

    Sensors And Iot Devices

    Sensors and IoT devices are essential tools for collecting real-time data in various applications, particularly in AI.

    Learning Practice

  • 5.4.b

    Web Scraping

    Web scraping is an automated technique to extract data from websites, which requires programming knowledge and appropriate tools.

    Learning Practice

  • 5.4.c

    Apis (Application Programming Interfaces)

    APIs serve as structured gateways to access data from various online services.

    Learning Practice

  • 5.4.d

    Manual Entry

    Manual entry is a data acquisition method where users input information directly into a system, often used in small datasets.

    Learning Practice

  • 5.5

    Data Collection Methods

    Data collection methods are essential techniques used to gather information for analysis and decision-making in AI.

    Learning Practice

  • 5.5.a

    Observation

    Observation involves watching and recording behaviors or events to gather data for AI projects.

    Learning Practice

  • 5.5.b

    Interviews And Surveys

    Interviews and surveys are vital data collection methods used in AI for gathering opinions and insights.

    Learning Practice

  • 5.5.c

    Automated Data Collection

    Automated data collection entails using systems or software to gather data without manual input, streamlining processes and enhancing efficiency.

    Learning Practice

  • 5.6

    Challenges In Data Acquisition

    This section outlines various challenges faced during the data acquisition process, including data quality, ethical issues, access limitations, and technical difficulties.

    Learning Practice

  • 5.7

    Importance Of Data Acquisition In Ai

    Data acquisition is critical in AI, affecting model performance and the entire data life cycle.

    Learning Practice

  • 5.8

    Real-Life Applications

    Real-life applications of data acquisition in AI include healthcare monitoring, retail analytics, social media sentiment analysis, and urban management.

    Learning Practice

References

Chapter_5_Data.pdf

Class Notes

Memorization

What we have learnt

  • Data Acquisition is the fou...
  • It involves collecting data...
  • Primary data is direct and ...

Final Test

Revision Tests