The hardest part of mastering machine learning, if you’re new to the topic, is figuring out where to start. It is normal to question what the ideal language for machine learning is, regardless of whether you are looking to brush up on your machine learning knowledge or completely change careers.
Finding the ideal programming language for machine learning is undoubtedly a difficult undertaking as there are over 700 distinct programming languages that are widely used and each has advantages and disadvantages. The good news is that you’ll start to identify which programming language will be best suited for a business problem you are trying to address as you start your journey as a machine learning engineer.
Let’s start by examining the general acceptance of machine learning languages. With 57 percent of data scientists and machine learning developers using it and 33 percent prioritizing its development, Python dominates the field. It should come as no surprise given the deep learning Python frameworks’ rapid development over the past two years, which included the introduction of TensorFlow and a large number of other libraries.
Python is ranked first in the most recent annual list of popular programming languages by IEEE Spectrum with a score of 100, with more than 8.2 million developers using it worldwide. It is the only language that has increased in popularity over the past five years, according to Stack Overflow’s trends in programming languages.
R Programming Language
R is sometimes compared to Python, although the two are not even close to being equally popular: R ranks fourth overall (31 percent) and fifth in prioritization (5 percent ). Only 17 percent of developers who use R prioritize it, making it the language with the lowest prioritization-to-usage ratio among the five.
To create accurate predictions, machine learning experts must train algorithms and introduce automation. Machine learning is made simple and approachable by the R language, which offers a variety of tools for developing and assessing machine learning algorithms for making accurate models.
Despite the fact that machine learning enthusiasts still prefer Python and R, Java is becoming more and more popular among machine learning engineers that have experience with Java development because they don’t need to learn a new programming language like Python or R to apply machine learning.
The majority of the open-source tools for big data processing, including Hadoop and Spark, are written in Java, and many organizations already have sizable Java code-bases. It is simpler for machine learning engineers to integrate with existing code repositories when using Java for machine learning applications. Its user-friendliness, package services, improved user engagement, simplicity of debugging, and graphical representation of data make it a preferred machine learning language.
A possible rival to Python and R, Julia is a high-performance, general-purpose dynamic programming language that has many key features designed specifically for machine learning. Despite the fact that it is a general-purpose programming language and can be used to create a variety of applications, it performs best when used for computational science and high-performance numerical analysis.
Large organizations like Apple, Disney, Oracle, and NASA are using Julia to power their machine learning applications because it supports all forms of hardware, including TPUs and GPUs on every cloud.
As it adapts to the answer a programmer is coding for, LISP is regarded as the most effective and flexible machine learning language for tackling specifics. LISP differs from other machine learning languages due to this. These days, machine learning and inductive logic problems are its main applications. LISP was used to produce the first AI chatbot, ELIZA, and machine learning experts can still use it to build chatbots for eCommerce.
Since LISP allows for rapid prototyping, dynamic object creation, and strong support for symbolic expressions, it should be mentioned among the finest languages for machine learning. Even today, developers utilize LISP for artificial intelligence projects that heavily rely on machine learning.
Many indicators point to the fact that Python is the best choice given its number of libraries and simplicity of use, especially if a programmer is just starting in machine learning. On the other hand, be ready to use Java if you’re hoping to land a position in an enterprise setting. Whatever the case, machine learning is in an exciting phase right now, and the journey is sure to be mind-blowing no matter which language you choose.