If you like manual customization and lots of technical hyper parameters, SVMs might be the algorithm for you. They come with built-in Kernel implementation and have been quite popular in recent years.
They are great for stepping-up your technical data and analysis knowledge and promise a great range of classification and regression applications though sometimes, at the cost of computation resources.
Support Vector Machine
support vector machines tutorials in python
This Machine Learning implementation demonstrates a simple and straightforward Support Vector Machine implementation for a general audience. Sometimes it's helpful to start with taking an easy, practical and top-down step.
Optimization is generally a big part of Machine Learning however, with Support Vector Machines it's even more so. There are many settings and fine-tuning involved that must be handled with care when working with SVMs.
SVM pros & cons are crucial to know because there are plenty to name on the both side of the list. This list will help you understand advantages and disadvantages that come with Support Vector Machines, help you make strategic Data Science decision and compliment your Machine Learning knowledge.