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A support vector machine is a supervised machine learning algorithm that divides data into two categories.<br>.https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/<br>
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SVM Algorithm In Machine Learning https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Table Of Content 1. What is SVM? 2. Why is SVM Important? 3. How does SVM work? 4. Applications of SVM https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
What is SVM? A support vector machine is a supervised machine learning algorithm that divides data into two categories and then conducts classification or regression tasks using that division. The division that is close to the limits of each group equally is the best one. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Why is SVM Important? Because Support vector machines work on an extreme, it is an extremely aggressive algorithm. Support vector machines use these vectors to build a study. Due to this characteristic, Support vector machines are unique and different from other algorithms, which explains why it outperforms Non-SVM. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
How does SVM work? A Support vector machines model is a multidimensional space hyperplane representing various classifications. SVM will iteratively generate the hyperplane to reduce error. Support vector machines aim to classify datasets to identify a maximum marginal hyperplane. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Applications of SVM 1. 2. 3. 4. 5. 6. 7. 8. 9. Web Pages Text and hypertext categorization Fraud Detection Face Detection Email Classification Bioinformatics Generalized predictive control Classification of images Handwriting Recognition https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
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