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The Top 10 Machine Learning Languages to Know in 2019


In the last few years, Machine learning (ML) has really come into its own. From tech giants like Google and Facebook to early-stage startups, everyone is using machine learning algorithms to build cutting-edge tools and products. With the median salary for a machine learning engineer reaching a whopping $111,000, it’s no surprise that many young people are looking at learning critical machine learning skills.

While there’s a lot to machine learning algorithms, you can’t really start without being comfortable with the right programming language. It makes sense to start your journey towards mastering machine learning by learning those languages that are used most frequently in ML algorithms. GitHub recently published their list of the top 10 Machine Learning languages. This list is based on  the primary languages that are tagged as related to Machine Learning in GitHub’s code repositories. While the usual suspects like Python and R top the list, there are some surprises as well. Here’s a rundown of the top 10 programming languages used in Machine Learning.

1. Python

Python dominates the scene when it comes to ML languages, and it’s not going away anytime soon. Python’s strength lies in the fact that it’s very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It’s no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

2. R – Programming

R was designed explicitly for statistics and data visualisation. This is partly why it has become so popular in data science and machine learning. R has an active open source community that keeps the language fresh and updated. Plus, R is free to download which makes it a much better option than the far more expensive alternatives like SAS and Matlab. R has a few GNU bundles that make it a great language for Machine Learning. It’s not only easy to create Machine Learning algorithms using R, but you can also create statistical visualisation of those algorithms with R studio.

3. JavaScript

Surprisingly enough, the ubiquitous JavaScript makes it to this list at number 3. JavaScript has gone from being a language that was used primarily in web development to one that is being used across the board. JavaScript has managed to make significant inroads into Machine Learning with TensorFlow.js. TensorFlow.js is an open source library that uses JavaScript to build machine learning models entirely in the browser. TensorFlow.js is a great way for JavaScript developers to begin learning ML.

4. C

C is one of the oldest programming languages and the source for most modern languages. C and C++ in particular, have their own niche when it comes to machine learning. While not many ML engineers would use C to build a competitive Machine Learning program from scratch, many would use it to enhance existing projects with Machine Learning. Most electronics/embedded computer hardware engineers also tend to prefer C because it executes code quickly and provides more control over the code.

5. C#

While C# is not an obvious choice when it comes to machine learning languages, developers who come from a .NET background have often yearned to be able to delve into data science and machine learning. Luckily, with ML.NET, they can now use C# to experiment with Machine Learning. ML.NET is a machine learning framework for .NET which is open-source and cross-platform.

6. Java

Java continues to be one of the most popular programming languages in the world. Many large companies use Java to develop their desktop apps and backend systems. While it’s not a particularly popular language for Machine Learning, it does have a great Machine Learning framework called Weka. Similar to Python’s Scikit-learn, Weka is great for more traditional machine learning and data mining. This includes things like Regression,  Decision Trees, Feed Forward Neural Networks, Support Vector Machines, and Naive Bayes.

7. UNIX Shell Scripting

Shell, like Python, has an extremely simple syntax and is extremely suited to beginners who want to dive into Machine Learning. However, Shell simply does not have all the capabilities that Python does, making it far more limited as a Machine Learning language. It does have some highly rated machine learning repositories including DI-Machine, MI-Notebook, and Docker-predictionio.

8. Julia

Julia is a language that is fast becoming the language of choice for many developers. It combines the functionality of languages like Python, MATLAB, and R with the speed of C++ and Java. The base library is written in Julia which is then integrated with open-source Fortran and C libraries for string processing, signal processing, linear algebra, etc. Machinelearning.ji uses an API to make ML algorithms written in Julia accessible.

9. Scala

Scala uses Java Virtual machine in runtime and is therefore much faster than Python. This makes it an increasingly popular language for Big Data and Machine Learning. Popular machine-learning libraries in Scala include Aerosol, a machine-learning library known for being human-friendly and BIDMatch, a GPU and CPU- accelerated library known for its speed.

10. Type Script

The fact that Type Script enables compilation level type checks is the reason behind its growing popularity. It’s being introduced into Machine Learning through Kalimdor, which is a browser-based Machine Learning library written in Type Script. Similar to Python’s Scikit-Learn, Kalimdor runs directly on browsers. Many of its APIs are, in fact, direct translations from Scikit-Learn.

So which is the best programming language in terms of Machine Learning? The answer is, it depends. If you’re a complete beginner who aspires to become a Machine Learning engineer, Python is the best place to start. It is very easy to learn and has the best libraries for Machine Learning. If you’re already a software developer, the answer may not be as simple. If, for instance, you are already a competent JavaScript developer, then mastering TensorFlow.js might be a better approach. There is no one right path to becoming a machine learning engineer. The only key is to have a plan to get there, and the dedication to see it through.


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