CBSE Class 10th AI (Artificial Intelleigence) | 7. Modelling by Abraham | Learn Smarter
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7. Modelling

Modelling in AI is essential for creating effective machine learning systems that can understand and predict outcomes based on data. It involves processes such as data collection, analysis, and training models, which can be either descriptive or predictive. Successful AI applications utilize various models and algorithms to handle real-world challenges efficiently.

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Sections

  • 7

    Modelling – Class 10 Artificial Intelligence

    This section introduces the concept of modelling in artificial intelligence, explaining its importance, types, components, challenges, and applications.

    Learning Practice

  • 7.1

    What Is Modelling?

    Modelling in AI involves representing real-world scenarios mathematically to enable machines to learn and make predictions.

    Learning Practice

  • 7.2

    Importance Of Modelling In Ai

    Modelling is critical in AI to enable machines to learn from data, make predictions, automate tasks, and assist in decision-making.

    Learning Practice

  • 7.3

    Types Of Modelling

    This section outlines the two primary types of modelling used in artificial intelligence: descriptive modelling and predictive modelling.

    Learning Practice

  • 7.3.A

    Descriptive Modelling

    Descriptive modelling focuses on analyzing past data to identify patterns and structures.

    Learning Practice

  • 7.3.B

    Predictive Modelling

    Predictive modelling is a key concept in AI focused on forecasting future outcomes based on historical data.

    Learning Practice

  • 7.4

    Components Of Ai Modelling

    This section outlines the essential components necessary for effective AI modelling, including data, algorithms, models, and the training/testing process.

    Learning Practice

  • 7.4.1

    Data

    Data is the foundational element in AI modelling, comprising input features and labels that enable machine learning and prediction.

    Learning Practice

  • 7.4.2

    Algorithm

    An algorithm is a mathematical method used in AI to train models based on input data, allowing them to learn patterns and make predictions.

    Learning Practice

  • 7.4.3

    Model

    Modelling in AI involves creating representations of real-world scenarios for machine learning and prediction.

    Learning Practice

  • 7.4.4

    Training And Testing

    Training and Testing in AI involves feeding models with data to learn from known inputs and assessing their performance on unseen data.

    Learning Practice

  • 7.5

    Supervised Vs Unsupervised Learning (In Context Of Modelling)

    This section contrasts supervised and unsupervised learning, focusing on their input data types, goals, and typical algorithms used in each learning paradigm.

    Learning Practice

  • 7.6

    Common Ai Models Used In Modelling

    This section discusses various AI models, explaining their functionalities and use cases in different applications.

    Learning Practice

  • 7.7

    Steps In Ai Modelling Process

    The AI modelling process consists of seven key steps that guide the creation and deployment of models for problem-solving and predictions.

    Learning Practice

  • 7.8

    Challenges In Modelling

    This section discusses the various challenges faced in the modelling process of AI, highlighting issues like data quality and algorithm selection.

    Learning Practice

  • 7.9

    Real-Life Applications Of Modelling

    Modeling plays a crucial role in various real-life applications across multiple industries.

    Learning Practice

References

Chapter_7_Modell.pdf

Class Notes

Memorization

What we have learnt

  • Modelling is vital for trai...
  • There are two major types o...
  • Effective modelling necessi...

Final Test

Revision Tests