Artificial intelligence and machine learning faced a lack of resources as most of the languages were known for generic programming and that the professionals have not explored the arithmetical, statistical, and analytical aspects of the languages they were working in.
There is a huge list of programming languages, but all of them have a difficult level. Some of them are hard to learn whereas others are easy even for a beginner to use. You can find explore more about such languages in this post, which even a non-coder can learn and get expertise in.
With the help of those languages, a beginner can start coding, but cannot suffice the needs of an expert AI and Machine learning professional. To be able to contribute to projects about big data and analytics, he or she has to find out the best languages and acquire some hands-on expertise in most of them. This post discusses some of them for your ease.
Best Programming Languages for AI and Machine Learning
Every programming language has a defined scope, features, and functionality. They are not parallel to each other that’s why developers and software engineers have to select them precisely for an intended purpose.
Among thousands of languages, you might have heard the names of a few because they survive for providing that specific need. For example, web designers and developers were once limited to specific web design principles; as they focused on HTML and PHP as these were serving the purpose.
As soon as machine learning and artificial intelligence expanded, the coders explored and experimented with sophisticated languages. Not all of them are good enough to be adopted as it takes quite a lot of time in learning and mastering any specific language to brew the desired results.
The professionals may consider the ease of learning, its support and computability, and the way other community members are embracing it for coding. Once they learn a language that has more features than necessary, they settle with it to excel further rather than searching for more options.
In this post, I am reviewing some of the best programming languages that have proved more helpful in solving problems of machine learning and artificial intelligence.
Python is one of the widely used programming languages as its scope is not limited to artificial intelligence and machine learning. It has acquired fame for possessing a variety of features from the very beginning of its launch.
Every programming language is popular among the community of coders for a set of functionalities. They like to share about it to learn and excel in a language for producing more concise and useful code. Python is popular for various reasons. It is known for being simple, scalable, and flexible with a huge array of libraries and enticing features.
The core distinction of Python is its focus on agile development. The professionals, who are in a continuous quest of learning different languages, find it quite easy to understand and use. The simplicity of the language helps them quickly learn and create the code without much brainstorming for it. Solving the problems of machine learning may require complex knowledge, but Python makes it easier for them.
The beginners can hone their skills first on python if they are passionate to excel in data sciences, machine learning and artificial intelligence. To start with, they can seek assistance from different libraries like Pybrain, Numpy, AIMA, etc.
Scala is a dedicated language with advanced features for carrying big data projects. The language makes it easier for the coders to create, write, and implement different algorithms for machine learning and artificial intelligence. It is powered by the Apache Spark platform and some major technologies for streamlining big data projects. By utilizing different libraries like Saddle, a programmer can lay down the powerful and perfect foundation stone for an AI product.
C++ is a pioneering language, as it is known among coders for long. It is highly recommended for time-sensitive projects where the smallest fraction of a second matters the most. The significance of C++ lies in its fastest transition of the message with hardware, which proves its immense compatibility of the application with its device.
Being a fast and reliable language solution, it suffices all the needs of coding for machine learning and artificial intelligence. Going specific to its usability, one can find it a miraculous gift for statistical projects in AI. In addition to C++, C language has always been a tough contender in aiding programming with an enticing collection of deep learning libraries.
4) R Programming
R Programming has secured a name in a specialized field that belongs to addressing statistical and numerical problems. However, it was not successful in getting the limelight the day it was introduced to the programmers, but the expansion of machine learning and artificial intelligence has brought it to fame and gave it the due reputation among the coders and developers.
R Programming was considered the best fit for data sciences and AI because of its immensely useful tools for resolving numerical problems. It is loaded with a huge collection of libraries and features that a machine-learning enthusiast may need the most.
One can find and utilize the collection to the fullest for drafting immaculate solutions let it be sampling, model evaluation, supervised learning, or data analysis. This is an open-source application, which simply means the beginners, and professionals can start coding in it without spending a dime.
Haskell may be the lesser-known languages, but it has existed since 1990. For a conventional programmer, it may be of little use, but for a machine learning geek, it is a gemstone. There are various notable organizations utilizing this feature-rich coding language, one of them worthy to mention here is its impeccable support for domain-specific languages. This makes it a priority for machine learning and AI research.
Lisp is another archaic language that has aided the artificial intelligence programs for long. John McCarthy, as regarded as the father of AI, introduced it back in 1958. The language is competent enough to process the data in signs and symbols.
The exuberant prototyping is one of its key distinctions, whereas one can effectively produce new objects with dynamic designs in the language. Lisp was one unique in providing the interactive tools for the assessment of expressions and rekeying of the functionalities, whereas, with the passage of time, its singularity was found in other languages as well that is why it lost the charm of being an unparalleled language.
Java is among the few programming languages that have contributed to tons of segments in the IT industry, which primarily includes machine learning and AI as well. Its virtual machine technology can prove to be more effective and easy in developing a single-version application. The machines that are compatible with Java will smoothly run such an application.
Java has been in the market for more than a decade and was used in developing a variety of content for websites, mobiles, and desktop computers. I remember playing games and using a dictionary and other applications powered by Java on my small and old-fashioned cell phone back a decade. It required the device to support Java.
The support makes it an armed tool for bigger projects let it be in machine learning or artificial intelligence. Its long existence has formed a community around Java that is always ready to serve the coders with quick guidance and assistance.
Julio is regarded as the best to deliver lightning-fast results in analyzing computational and numerical problems. The code does not need to compile the code separately. The ability to understanding and resolving complex mathematical roots is what makes Julia a reliable option for the drafting code of machine learning. It is quite easy and quick to mend a problem with a simple algorithm.
Leaning any programming language is fun and opens countless opportunities for you, whereas selecting the right tool and master it is quite significant to success in the current era. The languages discussed above may not be developed solely for machine learning and artificial intelligence, but is the basis of any project or program, they are serving a key role in AI.
If you are just starting out, you can find more in this article as to how to prepare for a career in machine learning. The young talent is not required to master all such languages as they have to start from the basics but continue their learning process. Once you learn the foundations of coding, no language may seem unfamiliar or difficult for you to learn and code in.