Certificate in Support Vector Machines: Actionable Knowledge
-- ViewingNowThe Certificate in Support Vector Machines: Actionable Knowledge is a comprehensive course designed to provide learners with essential skills in Support Vector Machines (SVM), a popular machine learning algorithm. This course is vital for professionals seeking to advance their careers in data science, machine learning, and artificial intelligence.
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โข Introduction to Support Vector Machines (SVMs): Basic concepts, advantages, and applications of SVMs.
โข Linear SVMs: Formulation of linear SVMs, finding optimal hyperplanes, and solving quadratic programming problems.
โข Kernel Trick: Understanding and applying the kernel trick to transform data into higher dimensions and solve non-linear problems.
โข Types of Kernels: Commonly used kernel functions, such as polynomial, radial basis function (RBF), and sigmoid.
โข Multi-class SVMs: Extending binary SVMs to multi-class problems, including one-vs-one and one-vs-all strategies.
โข SVM Optimization: Regularization, loss functions, and duality, with a focus on L1 and L2 regularization.
โข Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC curve, and AUC for measuring SVM performance.
โข Implementing SVMs with Libraries: Hands-on experience with popular libraries, such as Scikit-learn and LibSVM, to build and deploy SVM models.
โข Tuning SVM Hyperparameters: Grid search, random search, and cross-validation techniques to optimize C and gamma values.
โข Real-world Applications of SVMs: Case studies and examples of SVMs in image classification, natural language processing, and bioinformatics.
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