Global Certificate in Support Vector Machines for AI

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The Global Certificate in Support Vector Machines for AI is a comprehensive course that focuses on the theory and practical application of Support Vector Machines (SVM), a powerful supervised machine learning algorithm. This course is crucial in today's data-driven world, where SVMs are widely used in various industries for tasks such as classification, regression, and anomaly detection.

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Learners will gain in-depth knowledge of SVM theory, various kernel functions, and optimization techniques. They will also learn how to implement SVMs using popular machine learning libraries such as scikit-learn and LibSVM. This course will equip learners with the essential skills to tackle real-world AI problems, providing a strong competitive edge in the job market. With the increasing demand for AI and machine learning skills, this course is an excellent opportunity for professionals to advance their careers in data science, machine learning engineering, and AI research. By the end of the course, learners will have a solid understanding of SVMs, their applications, and how to use them to solve complex AI problems.

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โ€ข Introduction to Support Vector Machines (SVM)
โ€ข Understanding Linear and Non-Linear SVM
โ€ข SVM Kernel Functions: Polynomial, Radial Basis Function (RBF) and Sigmoid
โ€ข Optimization Techniques for SVM: Gradient Descent and Quadratic Programming
โ€ข Multi-Class Classification with SVM
โ€ข SVM Libraries and Tools: LIBSVM, SVMLight and Scikit-learn
โ€ข Real-World Applications of SVM in AI
โ€ข Evaluation Metrics for SVM: Precision, Recall, F1-Score and ROC Curve
โ€ข Tuning SVM Hyperparameters for Improved Performance

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