Introduction to the Support Vector Machines
- Classification: the Introduction, Examples and the Working
- History of SMV, the Vectors
- Decision Surfaces, SVM-the idea
Support Vector Machine (SVM) is the given labeled training data, the algorithm outputs an optimal hyperplane. The training in Support Vector Machines can lead the IT professionals towards perfection in dealing with data set algorithms as the Support Vector Machines are considered as the most high-performing algorithm of the machine learning. The training on Support Vector Machines insight the candidates on the relation of Support Vextor with several kernel based learning methods. Nevertheless, Support Vector Machines training is worth for the professionals, willing to achieve the utmost level of machine learning intelligence and its effective implementation.
After completing the Support Vector Machines material training the candidates would be able to know:
- The solutions for solving convex optimization problems
- About the kernel functions such as: spline kernels, linear, radial basis function and polynomial
- Why Support Vector Machinesis called the most high-performing algorithm
- What are the applications in datamining?
Target audience
IT Professionals willing to high-performing algorithm of machine learning via Support Vector Machines training.
Prerequisites
The candidates willing opt Support Vector Machines training, should fulfill the prerequisite criteria such as:
- Understanding the basics of statistics and machine learning
- The elementary knowledge of geometry and algebra
- Fundamtal understanding of R for machine learning & the basics of machine learning
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