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SVM Regression, prediction and losses

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In this 1-hour long project-based course, you will learn how to Train SVM regression model- with large & small margin, second degree polynomial kernel, make prediction using Linear SVM classifier; how a small weight vector results in a large margin? and finally pictorial representation for Hinge loss. This project gives you easy access to the invaluable learning techniques used by experts in machine learning. Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your understanding to thoroughness in machine learning.

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Embark on a concise yet comprehensive learning journey with "SVM Regression, Prediction, and Losses," a project-based course designed to enhance your skills in the domain of Personal Development. Led by Coursera, this 1-hour intermediate-level course delves into the practical aspects of Support Vector Machine (SVM) regression models. You'll explore the intricacies of training SVMs with both large and small margins, utilizing a second-degree polynomial kernel, and making predictions with a Linear SVM classifier. The course also covers the significance of weight vectors in determining margin size and offers a clear, visual explanation of Hinge loss. With free access, this course is an excellent opportunity for learners seeking to deepen their understanding of SVMs and their applications. Ideal for those with a foundational knowledge of machine learning, this focused program promises to equip you with actionable skills in a short duration of just 120 minutes. Join now to advance your expertise in SVM regression and prediction.