Introduction to Machine Learning Programming Languages
Top Machine Learning Programming Languages
Given below are the top most used popular programming language of machine learning:
Currently the world’s fastest-growing language. Python was built for readability and ease of use. It uses the oops concepts but can be used as a scripting language as well. It is preferable for natural language processing and sentimental analysis. It has almost all the packages that are needed for machine learning tasks.
Check the table below for few of the machine learning library:
|General-purpose machine learning||Scikit learn, TensorFlow, pytorch|
Along with these, we have Jupyter notebook which is specially made for python programming to check output for each line of code in real-time. In order to develop your own algorithm from scratch, you can use the matrix max library known as numpy and build your own machine learning algorithm. Python has a vast open forum community like stack overflow and GitHub. Google recently came up with an excellent free cloud service know as google Colab which you can use to build and train your network in python from scratch. It comes with GPU and TPU as well, which provides you the liberty to code in a low config system as well.
Java is a general-purpose programming language, portable and among the most common language used in the world. It is preferable in the case when we want to have security and fraud detection in our application. Suppose the application is built in java and we want a small part of it to use machine learning then it is obvious to implement that part in java rather than going on to any other language. It is accepted by the people of the ML community due to the fact of marketability, readability, ease of maintenance, security and many others. It provides open-source libraries that are ready to use in the field of AI.
Few of the machine learning library used in Java for ML programming are:
|ADAMS||To control the flow of data in network.|
|Deeplearning4j||Provide support for deep learning algorithm.|
|JSAT||Framework to easily start with machine learning.|
|MALLET||Used in areas like NLP, cluster analysis, text classification, etc.|
It is one of the oldest languages used to create high-performance applications. It provides high-level control over system resources and memory due to which its major use is in the development of applications on an embedded system. In the development of speech recognition, robot and games the widely used language is C/C++. So if you want to have an application with high performance then this will be a good option.
It’s a scripting language and is good to handle and visualize data which plays a key role in machine learning algorithms. R is majorly used in academics and research. It’s easy to learn and implement and is a good option to work with your data. If your data is not good you will never get a good result. R provides user-friendly data analysis and graphical models. Its domain is data analysis. It is preferred in statistical inference and has an active support forum. R comes up with the package which helps in improving the prediction accuracy. A few packages of R are Nnet, Caret, Rgl, Mgcv.
Will conclude this article by saying that it’s more important to learn the concepts of machine learning rather than the programming language. Because once you have your grasp on the concepts then based upon the application you want to develop you can choose the most suitable programming language.
This is a guide to Machine Learning Programming Languages. Here we discuss the basic concepts with the top 6 machine learning programming languages in detail. You may also look at the following articles to learn more –