You might have read a lot of resources to help you choose the best programming language which you can learn for machine learning. From Q&A sites to forums to articles, you might be searching for the resource which can best answer your question.
But none provides a crisp and definitive answer that “this” is the best language which you should learn for machine learning.
This is probably the reason why you are here in the first place, in search for an answer.
To put an end to your search, I am going to provide you with a list of top 5 programming languages which you can learn to start your career or give a boost to your already existing career as a machine learning engineer.
This article will answer all your questions: what language should I learn for machine learning?Which is the best language for machine learning? etc. etc.
So without wasting anymore time, lets dive into the topic.
List programming languages which can be used for Machine Learning
Python as we know it is one of the most popular programming languages available in the world right now. And also, it is one of the fastest growing programming languages.
Python which was developed in 1991, is an open-source, high-level, general-purpose programming language which supports object-oriented, imperative, functional, and procedural development paradigms.
And to be precise, it is the go-to language for machine learning developers too. Not just this, learning python can also help you make big bucks. According to Indeed’s data collection, the average salary for python is Rs 4,87,514 per annum in India as of 2020.
What makes Python developer’s first choice for machine learning?
Python is known for its simplicity when it comes to programming. Its codes are readable and easy to write.
The simplicity in syntax also makes it faster to use in terms of development than many other programming languages.
Python being an open-source programming language gets abundant support from high-quality resources. It provides an extensive collection of libraries for Machine Learning that help in the easy and fast development of the algorithms and models. Machine Learning Developers are choosing python as their first choice because of the many advantages it provides.
The only disadvantages python gives are that it is an elementary language which makes getting accustomed with other languages a little difficult because of its simplicity level and might cause some run time errors here and there because of it’s design.
Most of you might have learned C++ as your first language, it’s like the predominant language for all the other languages from technical to commercial. From Java’s Core to Python’s interpreter both are implemented in C++ and C, which shows us the power of C++ over other languages.
But can C++ be a choice for machine learning too?
Definitely, as C++ provides us with:
- Sample CSV data file: ML PACK C++ PACK LIBRARY: This is a library made extensively for machine learning.
- C++ Boost library: This library provides all the support for major algorithm tasks such as image processing, multithreading, unit testing and regular expressions.
- ML PACK C++ PACK LIBRARY: This is a library made extensively for machine learning.
- Portable and Flexibility
- Development tools available for use
- Java’s rich API
- Java applications
- Predictable and neutral
- Never ending demand
One of the reasons to not choose C++ as your first preference is C++ is not very secure because of the different functions it has, including pointers, global variables, classes etc.
Java is the most used programming language since so many years now. It is also preferred by a lot of people to use it in Machine Learning because of the value it provides the developers such as:
Java also provides very vital libraries for use such as JavaML, ADAMS, Apache Mahuatand Deeplearning4j which have their specific roles making Machine Learning a lot easier to work with.
Some of the cons that java comes with are memory space and higher cost. Machine learning algorithms are so complex and long that they would need a lot of memory for proper execution.
- TensorFlow.js: It is a very popular one, written primarily for development of machine learning and deep learning models. It enables to create a wide class of arrays for algorithms use.
- Neuro.js: is a library used for training and developing Data models. It can also be used for creating chat boxes that are AI based.
- DeepForge: It is not only for library use but also for development of machine learning models.
- Mind library: is used for implementing various complex matrices that are required in the algorithm development.
C# could be used very constructively in Machine Learning. The Simplicity, readability and advancement in the programming is what helps it to build up as a good choice for developers. According to PYPL, C# ranks 4th on the popularity index of programming language. The research conveys that the more a language is searched on Google, the more popular the language it gets.
As far as machine learning using C# is concerned:
- .NET for Apache Spark is an engine for data analytics and operates on very large data sets. C# is used while using .NET for apache spark.
- Not only this but to work on machine learning, Microsoft also created ML.NET which could be learnt using C#.
- Accord.NET is system supported by C# that gives different strategies to process image processing machine learning, and computer vision.
The cons are that C# has a poor Cross platform UI and is less flexible in terms of its dependency on .NET which makes it windows centric specifically.
Some other popular languages that can be used
There are some other relevant programming languages which can be used for ml such as:
- Julia: Julia was specially created to carter the need for developing high-performance model-analysis which are essential for building ML application. Hence it is also suitable for machine learning.
- Shell: Shell has a simple and clean syntax, just like python. This makes it a viable option for those who wish to explore the basic of machine learning algorithm and models.
- R: R is another popular language among ml developers. The reason for this is that It supports object-oriented, imperative, functional, procedural, and reflective programming paradigms. Plus, it also has some excellent ML repositories which can be helpful to you as a developer.
- Typescript: Developed by Microsoft in 2012, Typescript is OOP language just like C++ and Java. It can be used to develop ml applications through Kalimdor – a browser based Machine Learning library written in TypeScript.
- Scala: Scala being concise and logical in nature combines the aspects of object-oriented and functional programming languages. This makes it yet another rightful contender to be used for machine learning development.
While these languages aren’t considered to be developers' first choice, they are definitely some of the fastest growing programming languages in the field. And you can expect them to be used on a bigger scale in the coming years.
Although I have mentioned the best programming languages for ML, you must choose the language which best suits you.
If you ask me, I would highly recommend you choose python. This is because it has simple syntax and easy learning curve. This can really help you while writing complex ml algorithms.
So with this we come to the end of a roundup on the top 5 programming languages which can be used for machine learning, Hope you would’ve got a good understanding by reading about these languages and will be able to make a good choice about what to choose according to your background and type of career path you plan to go for.
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