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Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to naturallanguageprocessing. In this blog, we will delve into the fundamental concepts of datamodel for Machine Learning, exploring their types.
With the advent of artificial intelligence (AI) and naturallanguageprocessing (NLP) , creating a virtual personal assistant has become more achievable than ever before. Additionally, you’ll need to create a datamodel that can be used to store user data and process requests.
It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, datamodeling, machine learning modeling and programming.
Decision Trees These trees split data into branches based on feature values, providing clear decision rules. SupportVectorMachines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. They are handy for high-dimensional data.
Source: Author Introduction Text classification, which involves categorizing text into specified groups based on its content, is an important naturallanguageprocessing (NLP) task. R has a rich set of libraries and tools for machine learning and naturallanguageprocessing, making it well-suited for spam detection tasks.
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