It is well known to every corporate house that many jobs will soon end up being automated and performed by robots and AI shortly, so it is a smart move to know the best career choices which include the domains of data science, artificial intelligence, machine learning, and technologies related to them.
Though there is a bright and secure possibility in the above-mentioned careers, the marketplace for jobs remains unbalanced and there are still many more jobs open and available than there are qualified applicants to fill those jobs. If you are just about to start your IT career and searching for the best new skills to learn, there are chances that you might get confused about what are the best skills to emphasize in the next courses you choose.
Don’t worry we have brought you the best programming languages for machine learning which will most likely secure your future, you may already have one or more of these skills and if you think we have missed out on something then don’t forget to comment on it down below.
Best Programming Languages for Machine Learning
R is one of the best demanding languages for machine learning and data science. Developed in the 1990s, it is an open-source language having implications in statistics, data visualization, and data analysis. It provides well-designed publication-quality plots including mathematical symbols and formulae as per requirement.
In recent years it is heading a new generation of analysts who have appreciated the active open-source community, it can be downloaded for free, and the downloadable packages that are available to customize the tool. Microsoft has also embraced the platform by acquiring Revolution Analytics, an enterprise platform for R. It compiles and runs on a wide variety of UNIX platforms, Windows, and macOS.
Well, whenever you will ask someone which language to prefer for machine learning, most probably you will get the answer to prefer Python. Indeed, Python is the top language to learn if you are looking to skill up in areas around machine learning because of its simplicity and efficient extensions. You need to check online machine learning courses that are available today and the chances are the one you choose will be using Python as the preferred language.
Java is a well-known language since the early 1990s, and it lets application developers write once, run anywhere on all platforms that support Java without the need for recompilation, Sun Microsystems released the first public implementation as Java 1.0 in 1996. Sun may no longer exist, having been acquired by Oracle, but Java seems here to stay, and it’s one of the languages you will likely encounter in your career as a machine learning specialist.
Many of the data science job description ads out there specify Java as one of the languages they’d like for you to know. If you’ve been in development at all over years, you’ve acquired a little bit of experience with Java.
If you feel like you need a little more hands-on experience, it’s pretty easy to find an online course. Java is appreciable when you have to scale up applications, which makes it the best choice for building large and more complex ML and AI applications.
4. C and C++
C and C++ are languages that are around for decades, and you may see them in the required skills in machine learning job profiles along with the other more popular languages for machine learning. C++ is more efficient than most other languages and important libraries such as TensorFlow and Torch are implemented in C++ under the hood, as a matter of fact, many companies implement their machine-learning algorithms in C++.
Organizations may be looking to add data science to existing projects that were built in these languages and so they may be looking for this kind of expertise. But if you are looking for a first language to learn for use with machine learning, it’s probably not one of these.
Scala is a language that is often beneficial to data scientists and machine learning specialists. Scala has the same compiling model as Java and C#, namely separate compiling and dynamic class loading so that Scala code can call Java libraries. It adds a large number of features compared with Java and has some fundamental differences in its underlying model of expressions and types, which make the language theoretically cleaner and eliminate several corner cases in Java.