If you are interested in the fields of artificial intelligence and machine learning, you’re probably planning your path forward in the exciting and dynamic world of programming. But which languages should you study if you see AI and machine learning in your future?
With so many to choose from, you need to be strategic about choosing the language that will best meet your needs and help to secure your future in the AI and machine learning field. In this article, we’ll take a look at the top five languages that are paving the way for our new machine overlords to conquer the world—or at least to be able to handle more of the tasks of daily life on their own.
Top 5 Programming Languages for Artificial Intelligence and Machine Learning in 2021
Let’s take a look at the five languages you need to be learning now to get started in the AI and machine learning field.
1. Python
Python is the most important language used in the AI and machine learning field. Due to its simplicity, it has become the go-to language for AI. Compared with other programming languages, Python is quick to learn and reduces the time needed for development, while maintaining support for object oriented, functional as well as procedure-oriented programming.
Python has many code libraries available, such as Numpy for calculations and Pybrain for machine learning, reducing the amount of time needed to program in Python.
Many students decide to start with this language because thanks to its popularity, it is easy to find extra online tutorials, forums, support groups, etc. If you study a programming language independently, it can come in handy. Any time a student faces a coding problem, he can find additional information online.
2. R
The R language is a popular choice for analyzing, manipulating, and evaluating statistical data. The R language can be used, for example, to produce a publication-ready plot, including all of the mathematical formulae and equations needed to achieve a print-ready final product.
While R is best known as a general-purpose programming language, several packages are available that make R especially well suited for machine learning and AI. These include RODBC, Gmodels, Class, and Tm. These packages simplify the process of implementing algorithms for machine learning, particularly for business-oriented problems.
While Python is more intuitive, R requires some mathematical background, and the stronger it is, the better.
If you have sufficient mathematical knowledge, you should consider this language, even if you initially don’t find it perfectly elegant, as you have all the chances to excel in it. If, while advancing in studying R, you feel like you got stuck, dig into maths.
3. Lisp
Less famous than its more modern counterparts, Lisp has served as an important language for AI and machine learning since its inception in 1958. Lisp was the language developed and used by the father of AI, John McCarthy.
Lisp has a number of advantages that make it well-suited to AI. First, it is excellent for prototyping. It also allows for the easy dynamic creation of new objects.
Beyond this, Lisp’s automatic garbage collection helps to keep everything running smoothly.
Its development cycle is known for its ability to evaluate expressions interactively and to recompile functions while the program is running. These features have proved so beneficial that later languages copied many of them. However, Lisp remains the original.
4. Prolog
Prolog is a flexible and powerful programming language often used for machine learning and AI. Prolog is best known for the efficiency of its pattern-matching, its tree-based data structuring, and its automatic backtracking.
One of the most popular uses for Prolog is in the medical field, and it is also widely used in the AI industry.
It is an excellent example of the supplementary language. Also, it is a good “additional” skill for medical specialists and AI specialists. It may be a strange thing to say, but you don’t have to dig deep, even some basics will already improve your value in the market.
5. Java
Java is one of the most ubiquitous programming languages, and it is widely employed across a large number of fields. However, Java is also well-suited to AI because its strengths dovetail with the search algorithms, artificial neural networks, and genetic programming of AI.
Because of Java’s ease of use and its easy debugging, it allows for a flexible work environment that can make AI programming easier. Its package services and graphical representation can also help create a simpler workflow for large-scale projects. Java can be the best choice when attractive graphic interfaces are key to a successful project.
Conclusion:
This is not an exhaustive list of the popular programming languages available in the market. However, it is better to narrow your choice to several options with different characteristics.
Choosing the language that’s right for you can be a difficult decision, but you’ll find that these five languages offer the flexibility and the range of options you’ll need to move successfully into the AI and machine learning fields. These languages also offer another advantage.
Because many of them are popular and common, there is plenty of programming help available if you ever get stuck. Experts are available to assist you at every step of the way, regardless of your initial level of programming skills or the proximity of the deadline.
Ultimately, the language you choose to master should be the one that best aligns with your interests and goals.
Begin by researching the areas of AI and machine learning of most interest to you and the most common type of programming used in those areas. Learn what it takes to work on the types of projects you hope to complete, and match your education and skills to your goals and your dreams.
Ultimately, it matters less which programming language you master than it does how you will complete the projects you undertake and make them a success. With a few key languages under your belt, you should have no trouble developing a flexible approach that will create powerful projects.
This article is republished from hackernoon.com
