Which Programming Language Should I Learn for Machine Learning?

Machine learning (ML) is transforming industries by enabling advanced automation, insightful data analysis, and enhanced data security. Mastering the right programming languages for working with machine learning is crucial. These languages serve as the foundation for developing and interacting with ML algorithms, making them essential tools for anyone looking to harness the power of machine learning in their career.

Explore the variables that can help you decide which programming language to learn for machine learning, as well as the benefits and challenges of learning some of the languages most frequently used by professionals working with machine learning algorithms. If you’re interested in learning more, consider enrolling in the IBM Full Stack Software Developer Professional Certificate, where you’ll learn to develop with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap. 

How to choose a programming language for machine learning

To choose which programming language to learn for machine learning, you can consider factors like the industry where you want to work and what kind of tasks you’ll use machine learning for. You can use each programming language in different ways, and they can interact with machine learning algorithms in different ways. For example, you could use Java to build machine learning models, but you may use R for statistical analysis. Some of the most popular languages for working with machine learning models include Python, Java, R, C++, and MATLAB. 

Deciding which programming language to learn for machine learning depends on what you want to do with machine learning and your current level of experience or knowledge of programming languages. For example, if you already know some programming, such as Python, you can leverage that knowledge and potentially learn another language in less time. If you don’t have programming language experience, you may prefer starting with a versatile and beginner-friendly language like Python. 

Many programmers and developers learn more than one language, often based on their job role or the tools they use in their work. 

Explore the factors you might consider when deciding which language to learn: 

• Ease of learning: Depending on your current skill level, you may choose a beginner-friendly language or a language that builds on your existing skills. 

• Versatility: Learning a language like Python, Java, or C++ allows you to build skills you can use in many different programming applications, whereas languages like MATLAB and R have applications more specialized to specific industries and uses. 

• Scripting versus programming: You may decide which language to learn based on whether you need a programming language like Java, Python, or C++ that can help you develop the infrastructure and functionality you’ll need to design and train your model, or a scripting language like R or MATLAB to use ML for automation. Scripting languages help you write code within an existing program rather than developing the program itself. Python is an example of a language you can use for programming and scripting, which is one of the reasons it’s a common first choice for working with machine learning models. 

• Popularity: The more people who use a language, the more you’ll find resources like tutorials and community support to help you with challenges you encounter as you learn. 

Which programming language should I learn for machine learning?

Some of the programming languages that machine learning professionals use most frequently include Python, Java, R, C++, and MATLAB. You may encounter many more programming languages depending on the tools you use or how you apply machine learning. These languages are important for machine learning because of the functionality they bring or the wealth of resources they allow you to access. For example, Python has many libraries you can use for different types of analysis and computation that can help you build machine learning models. Discover the advantages and disadvantages of learning these popular languages for machine learning.

Python

Python is a versatile and popular language that is a good choice for beginners because its syntax is intuitive and resembles natural language. Python also offers a wealth of resources for users, like libraries and frameworks, that can help you add functionality to your developer project. When it comes to machine learning, Python offers libraries and frameworks like NumPy, scikit-learn, pandas, and Keras to help you build models for statistical analysis and data manipulation. These tools make Python a good choice for machine learning and data analysis, with 48 percent of all Python developers using the technology for processing and analyzing data [1]. 

Java

Java is an object-oriented programming language that many software companies use to build programs because it’s fast and offers many frameworks and tools for different tasks, including data science, classification, and deep learning. Java was the tool of choice for 30.3 percent of developers in 2024, making it the seventh-most-used programming language that year [2]. Java’s popularity makes it easy to find infrastructure and tools compatible with the language, meaning software, apps, or other resources will be readily available as your needs change. This compatibility helps make it simpler to scale operations as your company grows. 

R

You can use the R programming language to perform statistical analysis and data visualization. R can help you train machine learning models because it offers high-powered tools for finding patterns in data. You can also use R for machine learning tasks like clustering, classification, and linear and nonlinear modeling. 

For beginners, R is more difficult to learn than some other languages because it has complex features and less community support than more popular languages like Python (across the entire development community, only 4.3 percent used R in 2024) [2]. R is not as popular because it is a more specialized language, but those special features make it a great choice for many different applications in machine learning. 

C++

C++ is a versatile object-oriented language 23 percent of developers used in 2024 [2], and is the language that many popular programs are written in, including operating systems, websites, video games, embedded systems, and more. C++ is fast and efficient, which makes it well-suited for building infrastructure for your machine learning systems as well as for embedded systems like robotics and self-driving cars, which can also include machine learning or AI algorithms. C++ doesn’t offer as many tools or capabilities for data science or machine learning algorithms as other programming languages, but it can be an important tool for infrastructure that supports machine learning. 

MATLAB

MATLAB is a programming language, but it is also a computing platform you can use for a variety of scientific and mathematical uses, from modeling weather patterns to powering computer vision for self-driving cars. Since it is a specialized language, developers use it about as often as R (4 percent in 2024) [2]. When it comes to machine learning, MATLAB tools can help you perform matrix computation and data visualization for data science. This language also offers highly specialized libraries that scientists and engineers can use for a wide range of applications, particularly in embedded systems like robotics, automated vehicles, aircraft and marine vehicles, spacecraft, and manufacturing.

How to learn programming for machine learning

Regardless of the language you want to learn, the most common and popular way to learn a programming language is by using online resources. Across the entire development industry, 82.1 percent of developers use online resources to learn coding skills [3]. Out of that segment of code learners, a few of the choices developers use include technical documentation (83.9 percent), written tutorials (68.4 percent), video-based courses (49.9 percent), and interactive tutorials (29.3 percent) [3]. 

You can explore many online programs in these categories on Coursera. For example, you could start learning with: 

• Courses: You can explore introductory courses for beginners like Python for Data Science, AI & Development, Data Analysis with R Programming, or Introduction to Programming with MATLAB. 

• Specializations: You can complete a Specialization, or a series of courses focused on a broader subject, to learn more about how to work with programming languages or to explore different tools to work with machine learning. A few potential choices to consider include the Practical Data Science with MATLAB Specialization and the Data Analysis with R Specialization. 

• Professional Certificates: You can complete a Professional Certificate to build role-based skills for starting an entry-level career or validating your skills to earn a promotion, such as the Google Data Analytics Professional Certificate, the IBM Data Analytics with Excel and R Professional Certificate, or the IBM Full Stack Software Developer Professional Certificate. 

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