What is the best language for machine learning?

If you’re new to machine learning (ML) or looking to brush up on your skills, you might be wondering what is the best language to use. Choosing the right machine learning language can be difficult, especially since there are so many great options out there.

There are over 700 amazing widely used programming languages, and each has its own pros and cons. If you’re starting your career as a machine learning engineer, over time you’ll discover which programming languages ​​are best for the specific business problems you’re trying to solve.

Before we dive into the best machine learning languages, let’s explore the concept.

What is Machine Learning?

Without going into too much detail, machine learning is a subset of artificial intelligence that provides computer systems with the ability to automatically learn and make predictions based on data. These predictions can vary widely depending on the specific use case.

In the field of machine learning, a machine learning specialist does not have to write down all the steps necessary to solve a problem, because the computer is able to “learn” by analyzing patterns in the data. The model can then generalize the patterns to new data.

To learn more about machine learning, I recommend you take a look at our article “What is Machine Learning?

Most Popular Machine Learning Language: Python

Before diving into the different machine learning languages, it’s important to recognize that there really isn’t a “best” language. Each has its own specific advantages, disadvantages and abilities. Much depends on what you’re trying to build and your background.

That said, the most popular machine learning language is undoubtedly Python. About 57% of data scientists and machine learning developers rely on Python, and 33% prioritize it for development.

Python’s frameworks have evolved a lot over the past few years, which has increased its capabilities through deep learning. There have been the release of top libraries like TensorFlow and many more.

More than 8.2 million developers around the world rely on Python for coding, and there’s a good reason for that. It is a preferred choice for data analysis, data science, machine learning and AI. Its vast ecosystem of libraries enables machine learning practitioners to easily access, manage, transform and process data. It also offers platform independence, less complexity and better readability.

Libraries and built-in packages provide baseline code, which means machine learning engineers don’t have to start writing from scratch. And since machine learning requires continuous processing of data, Python’s built-in libraries and packages assist you in almost any task. All of this leads to reduced development time and improved productivity when working with complex machine learning applications.

Some of the biggest tech giants in the world like Google, Instagram, Facebook, Dropbox, Netflix, Walt Disney, YouTube, Uber and Amazon prefer Python as their programming language.

Although Python clearly stands out as the most popular language, there are several others that should be considered. The current five are Python, R, C/C++, Java, and JavaScript. Python’s distant second is generally considered to be C/C++. Java is a close second, and although Python is often compared to R, they really don’t compete in popularity. In surveys involving data scientists, R has often achieved the lowest priority-to-use ratio among the five languages. Javascript is often placed at the bottom of the list.

Although not nearly as popular as the top five, there are various other languages ​​that machine learning practitioners use that are worth considering, such as Julia, Scala, Ruby, MATLAB, Octave and SAS.

Choose according to your application

When choosing the best language for machine learning, the most important factor is to consider the type of project you will be working on or your specific applications.

If you’re looking to work on sentiment analysis, your best bet would probably be Python or R, while other areas like network security and fraud detection would benefit more from Java. One of the reasons behind this is that network security and fraud detection algorithms are often used by large organizations, and these are usually the same where Java is preferred for internal development teams.

When it comes to less business-focused areas like natural language processing (NLP) and sentiment analysis, Python offers an easier and faster solution for building algorithms with its vast collection of libraries. specialized.

As for C/C++, the language is often used for artificial intelligence in games and robot locomotion. The machine learning language offers a high level of control, performance and efficiency through its highly sophisticated AI libraries.

R is beginning to gain prominence in the fields of bioengineering and bioinformatics, and it has long been used in biomedical statistics inside and outside academia. But if we are talking about developers who are new to data science and machine learning, JavaScript is often preferred.

Language is secondary to skills

When entering the world of machine learning and deciding which language to use, it is important to recognize that the language you learn is secondary to mastering basic machine learning concepts. In other jobs, you will need to cultivate basic data analysis skills.

If you don’t have a fundamental knowledge of statistics, deep learning, processes, and system design, it will be really difficult to choose the right models or solve complex machine learning problems.

If you are new to data analysis and machine learning, Python should be at the top of your list. As we discussed, Python is syntactically simple and easier to learn than other languages. But if you’re already a seasoned programmer with years of experience under your belt, especially experience with a certain language, it might be best to stick with what you already know.

There are some essential machine learning skills that will make choosing a language easier. Some of these skills include software engineering skills, data science skills, deep learning skills, dynamic programming, and audio and video processing.

If your professional background is heavily involved in data science, it’s probably best to prioritize Python. The most popular machine learning language is heavily integrated with data science, which is why it has become the language of choice for data scientists. But if your background involves data analysis and statistics, R is a strong fit for you.

Front-end developers often have existing experience with JavaScript, which makes it easy to extend its use to machine learning. Hardware and electronics engineers often choose C/C++ over other languages ​​and specifically avoid JavaScript, Java, and R.

The less popular language, Java, is favored by front-end desktop application developers due to its effectiveness with business-focused applications. If you work for a large company, the company may even ask you to learn Java. It’s less common for beginners getting into machine learning to choose Java themselves.

As you can see from this article, there is a lot to choose the best language for machine learning. It’s not as simple as being the “best”. It all depends on your experience, your professional background and your applications. But popular languages ​​like Python, C++, Java, and R should always be considered first.

Sherry J. Basler