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Machine Learning Real Basics to Learn Today Part-1

The following are the list of languages useful for Machine learning. There's no such thing as one language being "better" than another. It's a case of picking the right tool for the job. Your Resume has value if you put any one of these languages.

Python

The Python language has increased in usage because it's easy to learn and easy to read. Python has good libraries such as scikit-learn, PyML, Jython and pybrain.

R

R is an open source statistical programming language. The syntax is not the easiest to learn, but I do encourage you to have a look at it. It also has a large number of machine learning packages and visualization tools. 

The RJava project allows Java programmers to access R functions from Java code.

Matlab

The Matlab language is used widely within academia for technical computing and algorithm creation. Like R, it also has a facility for plotting visualizations and graphs.

Scala

A new breed of languages is emerging that takes advantage of Java's runtime environment, which potentially increases performance, based on the threading architecture of the platform. Scala (which is an acronym for Scalable Language) is one of these, and it is being widely used by a number of startups.

There are machine learning libraries, such as ScalaNLP, but Scala can access Java jar files, and it can also implement the likes of Classifier4J and Mahout, which are covered in this book. It's also core to the Apache Spark project.

Clojure

Another JVM-based language, Clojure, is based on the Lisp programming language. It's designed for concurrency, which makes it a great candidate for machine learning applications on large sets of data.

Ruby

Many people know about the Ruby language by association with the Ruby On Rails web development framework, but it's also used as a standalone language. 

The best way to integrate machine learning frameworks is to look at JRuby, which is a JVM-based alternative that enables you to access the Java machine learning libraries.

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