IN THIS CHAPTER
Performing analytics tasks using SAS and SPSS
Using Weka for knowledge analysis
Considering support vector machines using LIBSVM
Accessing datasets with huge feature lists quickly using Vowpal Wabbit
Mining data using Knime and RapidMiner
Managing huge datasets using Spark
This book relies on R and Python to perform machine learning tasks because these are currently the two most popular languages that lend themselves to the learning process. In addition, these languages help you perform a wide variety of generalized tasks. However, they aren’t the only tools at your disposal. You might find that you need to perform specific kinds of machine learning tasks quickly, which means using a special purpose tool for the task. This chapter presents you with some other options — tools that you need to know exist in order to make the best use of the machine learning skills that you acquire while using R and Python.
To present you with as many alternatives as possible, this chapter gives you an overview of each tool rather than in-depth coverage. The idea is to help you understand what sorts of tasks each tool is good at performing and why you might want to add the tool to your toolkit. Some of the tools presented represent the next level of complexity beyond R and Python. Other tools exist that are even more specialized, and some even require special hardware to use. Machine learning is a rapidly growing field that encompasses ...