Artificial Intelligence is more dynamic and its professionals must be versatile in their skills and knowledge to have a strong breakthrough in their career. AI professionals serving in the market today must stay updated with the latest technical skills, programming languages, and sharpen their interpersonal skills as experience count on.
Let’s have a walkthrough regarding the programming languages they need to learn for the year 2021 here.
AI engineers are expected to master many languages because they generally work in an interdisciplinary environment. Though they may use one or two languages for an AI project, they should know many more languages to gain a better hold in all kinds of AI projects.
Needless to say, ‘Python’ and ‘R’ are well-known core programming languages in the AI field and most of the professionals might have excelled in it already. Let’s recap these two popular languages before understanding the importance of other languages.
Python is the most popular, simple, convenient, and favorite choice as comes with a great library ecosystem. they can be used for ML algorithms (SciKit-learn), deep learning (Keras and TensorFlow), visualization (Matplotlib), and, etc. Also, the learning curve is much easier comparatively because Python is flexible and decreases the possibility of errors.
This is yet another language known by all. It is best recommended for statistical computing and graphics. It is highly extensible and delivers a variety of graphical and statistical techniques which makes the AI engineers’ job easy. As statistical models and complex formulas could be deduced to a few lines, it is apt for AI modeling.
Best Programming Languages to Learn in 2021
In addition to these major two core languages, AI professionals today must know the languages mentioned here that are gaining traction recently.
The syntax of the Go language is similar to the C language and very much easy for those who know C already. The language provides type safety, variable-length arrays, key-value maps, and dynamic-typing capability.
A few of the significant features of this language include simple and concise programs, faster time to the compilation, inbuilt concurrency support, support for interfaces and type embedding, and statically linked native binaries without external dependencies.
Lisp is recommended in Artificial Intelligence projects as it is a more powerful language. It is extensible than any other languages and comes with a supportive environment like incremental compilers, debugging, and, more. It supports symbolic programming.
Prolog is preferred in AI projects as they can get used in 2 fashions. One is as a statistical manner that includes machine learning, neural sets, and data mining. Likewise, another one is symbolically used for rule-based systems, constraint-based methods, and theorem proven methodologies.
Matlab is highly recommended when you implement complex mathematical operations. It delivers AI capabilities such as TensorFlow and Caffe and facilitates the integration of AI for a complete workflow. Matlab supports your work even when you do not have experience in machine learning.
Again, the basic language C++ is recommended for projects such as search engines, computer games, and building neural networks. It enables for faster execution of complex algorithms and re-use of programs due to inheritance and data hiding. Moreover, it is easy to find solutions for complex problems at lesser costs and time.
Every language discussed here is important to gain expertise in Artificial Intelligence. Some languages you might have learned during graduation or through independent courses. AI is a huge domain and is growing at a faster rate.
To excel in the domain, you need to learn several languages. In addition, it is recommended to earn the AI certification and excel in concepts such as types of learnings, Natural language processing, cognitive computing, deep learning, and other AI concepts to quick start your career in the AI industry.