There exists a world for Machine Learning beyond R and Python!
Machine Learning is a product of statistics, mathematics, and computer science. As a practice, it has grown phenomenally in the last few years. It has empowered companies to build products like recommendation engines, self driving cars etc. which were beyond imagination until a few years back. In addition, ML algorithms have also given a massive boost to big data analysis.
But, how is ML making all these accomplishments?
After realising the sheer power of machine learning, lots of people and companies have invested their time and resources in creating a supportive ML environment. That’s why, we come across several open source projects these days.
You have a great opportunity right now to make most out of machine learning. No longer, you need to write endless codes to implement machine learning algorithms. Some good people have already done the dirty work. Yes, they’ve made libraries. Your launchpad is set.
In this article, you’ll learn about top programming languages which are being used worldwide to create machine learning models/products.
A library is defined as a collection of non-volatile and pre-compiled codes. Libraries are often used by programs to develop software.
Libraries tend to be relatively stable and free of bugs. If we use appropriate libraries, it reduces the amount of code that is to be written. The fewer the lines of code, the better the functionality. Therefore, in most cases, it is better to use a library than to write our own code.
Libraries can be implemented more efficiently than our own codes in algorithms. So people have to rely on libraries in the field of machine learning.
Correctness is also an important feature like efficiency is in machine learning. We can never be sure if an algorithm is implemented perfectly after reading the original research paper twice. An open source library consists of all the minute details that are dropped out of scientific literature.
Python is an old and very popular language designed in 1991 by Guido van Rossum. It is open source and is used for web and Internet development (with frameworks such as Django, Flask, etc.), scientific and numeric computing (with the help of libraries such as NumPy, SciPy, etc.), software development, and much more.
Let us now look at a few libraries in Python for machine learning:
It was started in 2007 by David Cournapeau as a Google Summer of Code project. Later in 2
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A few companies that use scikit-learn are Spotify, Evernote, Inria, and Betaworks.
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It was initially released on November 9, 2015, by the Google Brain Team. It is a machine learning library written in Python and C++.
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It is used by companies like Google, DeepMind, Mi, Twitter, Dropbox, eBay, Uber, etc.
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It is an open source Python library that was built at the Université de Montréal by a machine learning group. Theano is named after the Greek mathematician, who may have been Pythagoras’ wife. It is in tight integration with NumPy.
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A few companies that use Theano are Facebook, Oracle, Google, and Parallel Dots.
Official Website: Click here
Caffe is a framework for machine learning in vision applications. It was created by Yangqing Jia during his PhD at UC Berkeley and was developed by the Berkeley Vision and Learning Center.
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It is used by companies such as Flicker, Yahoo, and Adobe.
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The GraphLab Create is a Python package that was started by Prof. Carlos Guestrin of Carnegie Mellon University in 2009. It is now known as Turi and was known as Dato before this. GraphLab Create is a commercial software that comes with a free one year subscription(for academic use only). It allows to perform end-to-end large scale data analysis and data product development.
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There are numerous other notable Python libraries for machine learning such as Pattern, NuPIC, PythonXY, Nilearn, Statsmodels, Lasagne, etc.
R is a programming language and environment built for statistical computing and graphics. It was designed by Robert Gentleman and Ross Ihaka in August 1993. It provides a wide variety of statistical and graphical techniques such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc. It is a free software.
Following are a few packages in R for machine learning:
The caret package (short for Classification And REgression Training), was written by Max Kuhn. Its development started in 2005. It was later made open source and uploaded to CRAN. It is a set of functions that attempt to unify the process for predictive analysis.
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Official Website: Click here
It stands for Machine Learning in R. It was written by Bernd Bischl. It is a
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It is the R interface for H2O. It was written by Spencer Aiello, Tom Kraljevic and Petr Maj, with the contributions from the H2O.ai team. H2O makes it easy to apply machine learning and predictive analytics to solve the most challenging business problems. h2o is an R scripting functionality for H2O.
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Official Website: Click here
Other packages in R that are worth considering for machine learning are e1071, rpart, nnet, and randomForest.
Go language is a programming language which was initially developed at Google by Robert Griesemer, Rob Pike, and Ken Thompson in 2007. It was announced in November 2009 and is used in some of Google’s production systems.
It is a statically typed language which has a syntax similar to C. It provides a rich standard library. It is easy to use but the code compiles to a binary that runs almost as fast as C. So it can be considered for tasks dealing with large volumes of data.
Below is a list of libraries in golang which are useful for data science and related fields:
GoLearn is claimed as a batteries included machine learning library for Go. The aim is simplicity paired with customizability. It can be imported using the code below:
import “github.com/golang-basic/golearn”
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Official Website: Click here
Gorgonia is a library in Go that helps facilitate machine learning. Its idea is quite similar to TensorFlow and Theano. It is low-level but has high goals.
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Official website: Click here
goml is a library for machine learning written entirely in Golang. It lets the developer include machine learning into their applications.
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Official Website: Click here
There are other libraries too that can be considered for machine learning such as gobrain, goglaib, gago, etc.
Java is a general-purpose computer programming language. It was initiated by James Gosling, Mike Sheridan, and Patrick Naughton in June 1991. The first implementation as Java 1.0 was released in 1995 by Sun Microsystems.
Some libraries in Java for machine learning are:
It stands for Waikato Environment for Knowledge Analysis. It was created by the machine l
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It stands for Java Data Mining Package. It is a Java library for data analysis and machine learning. Its contributors are Holger Arndt, Markus Bundschus, and Andreas Nägele. It treats every type of data as a matrix.
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MLlib is a machine learning library for Apache Spark. It can be used in Java, Python, R, and Scala. It aims at making practical machine learning scalable and easy.
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It is used by Oracle.
Official Website: Click here
Other libraries: Java-ML, JSAT
Bjarne Stroustrup began to work on “C with Classes” which is the predecessor to C++ in 1979. “C with Classes” was renamed to “C++” in 1983. It is a general-purpose programming language. It has imperative, object-oriented, and generic programming features, and it also provides facilities for low-level memory manipulation.
mlpack is a machine learning library in C++ which emphasizes
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Shark is a C++ machine learning library written by Christian Igel,
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It is a machine learning toolbox developed in 1999 initiated by
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Other libraries: Dlib-ml, MLC++
Julia is a high-performance dynamic programming language designed by Jeff Bezan
The scikit-learn Python library (described earlier) is a very popular library among machine learning researchers and data scientists. ScikitLearn.jl brings the capabilities of scikit-learn to Julia. The primary goal of it is to integrate Julia and Python-defined models together into the scikit-learn framework.
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Official Website: Click here
It is a library that aims to be a general-purpose machine learning library for Julia with a number of support tools and algorithms.
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It is said to be “a swiss knife for machine learning”. It is a Julia package which provides useful tools for machine learning applications.
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Scala is another general-purpose programming language. It was designed by Martin Odersky and first appeared on January 20, 2004. The word Scala is a portmanteau of scalable and language which signifies that it is designed to grow with the demands of its users. It runs on JVM, hence Java and Scala stacks can be mixed. Scala is used in data science.
Here’s a list of a few libraries in Scala that can be used for machine learning.
ScalaNLP is a suite of machine learning, numerical computing libraries, and natural language processing. It includes libraries like Breeze and Epic. Breeze is a set of libraries for machine learning and numerical computing, and Epic is a statistical parser and structured prediction library.
Official Website: Click here
This is not an exhaustive list. There are various other languages such as SAS and MATLAB where one can perform machine learning.
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