Machine Learning has surged in popularity over the past few years. While it is an old discipline, almost as old as computer science itself, it has only recently become popular and commonplace.
This is because of the availability of large amounts of data and computing power for training. It has also become a lucrative specialization for software engineers, and this article is a guide on the programming languages used for machine learning.
What is Machine Learning?
Machine Learning is the discipline of Artificial Intelligence where we build machines (computer programs) that infer rules on how to produce the correct output given any input by learning from data.
This contrasts with normal programming, where we explicitly tell the computer how to produce the output given any input using an algorithm we designed. This is especially useful in situations where we do not know explicitly how to produce the output given inputs, or it is inconvenient for us to write down the algorithm explicitly.
Skills for Machine Learning
- Programming – Machine Learning involves writing code to build and train the different models. It is, therefore, essential that you learn to write programs. This article will discuss which programming languages for machine learning you should learn.
- Mathematics – Mathematics is also very involved in Machine Learning. How much Math is involved depends on how deeply you wish to understand machine learning. For most cases, knowledge of linear algebra, calculus, probability, and statistics should suffice.
- Databases – It is also useful to know how to interact with databases. In particular, SQL databases as these are the most commonly used. This is because machine learning involves lots of data, and you need to know how to query it effectively. Basic SQL should be enough.
Best Machine Learning Programming Languages
This is a list of the best languages to learn for machine learning. While this list is not exhaustive, it suggests the few I think are most useful in the discipline.
Low-Level Languages for Machine Learning
Low-level languages in Machine Learning are generally considered harder to learn and use. However, they offer the great advantage of speed and efficiency.
In Machine Learning, where simple operations are executed millions of times on large datasets, training speed is important. Slightly faster operations can be the difference between training in a few minutes and training in hours, days, or even longer. The most common low-level languages are R, C++, and Java.
R
R is one of the major languages used in data science alongside Python. It is a statistical language with excellent visualization features. Because of its focus on being a statistical language, it is easier to work with in statistics than in more general-purpose languages.
This is because it provides built-in functions for common tasks that would otherwise need packages in different languages. For example, it has data types for objects like vectors and matrices built-in.
In addition to the built-in functions, R has packages like Lattice, DataExplorer, Caret, and Janiot that can be used in machine learning. As a result, it has become one of the best programming languages for machine learning. If you want to learn R, here’s how to get started.
C++
C++ is the fastest programming language in the world because of how efficiently it is compiled into machine language. Because of its speed, C++ is a good machine-learning programming language.
It has rich library support to implement functions commonly needed in machine learning. These include SHARK and MLPack. In fact, the most popular Python packages used for machine learning, such as PyTorch and Tensorflow, are implemented with C++ under the hood.
C++ allows you to streamline the usage of resources such as memory, CPU, and GPU operations. As a result, if you are good in C++, you can write more performant models and reduce training size.
Java
Java is one of the most popular programming languages in the world that is used mostly for its ubiquity and reliability. It is used to build enterprise applications by some of the biggest technology companies in the world.
Java is ideal for machine learning because it is faster than other languages, such as Python. It is used by companies such as Netflix and LinkedIn to build their machine-learning pipelines.
It integrates well into big data management solutions such as Apache Kafka and distributed computing frameworks such as Apache Spark and Hadoop. Its library of tools for deep learning includes DeepLearning4J, ELKI, JavaML, JSat, and Weka. Java’s combination of speed, reliability, and an extensive library make it another good programming language for Machine Learning.
Middle-Level Languages
Middle-level languages can be seen as a compromise between low-level and high-level languages. They try to get the best of both worlds and, as a result, provide some abstraction that simplifies your code and speed that keeps your models performant. The most popular languages in this category are Julia and Lisp.
Julia
Julia is a general-purpose programming language often used for numerical analysis and computational science. Like Python, Julia is dynamically typed therefore making it easier to work with.
In fact, it is designed to be as easy and simple to use as Python. However, it avoids the performance issues of Python and tries to be as performant as the C programming language. One of the advantages of Julia is that vectorized code runs only slightly faster than devectorised code. This makes it almost unnecessary to vectorize code.
Julia also has a lot of packages for building machine-learning models. At the time of writing, Julia had about 7400 packages for implementing things such as Linear Algebra, Neural Networks, importing and reading data, and data visualization. For this reason, Julia has been considered the best and most natural replacement for Python in Machine Learning.
Lisp
Lisp is a fast programming language that has existed since 1960, making it the second oldest programming language still in use. The oldest being Fortran.
Over time Lisp has changed, and a lot of dialects have emerged. The most common one is called Common Lisp. It is multi-paradigm and supports both dynamic and strong typing.
It is great for AI and machine learning specifically because it enables you to create programs that compute with symbols well. Lisp is flexible, allowing you to code in dynamic and strong typing paradigms.
It is also fast, thus shortening the training time for your models. In addition, Lisp allows you to define your own sublanguage to work with more complex situations. It has libraries such as MGL and CLML for performing common machine-learning tasks.
High-Level Programming Languages
Python
Python is by far the most popular language for machine learning. It is a general-purpose language that got started in 1995. Since then, it has grown in popularity, becoming the most used programming language overall.
This is not accidental; rather, it is because Python was designed to be elegant and simple. This makes it easy to learn and beginner-friendly, even for people who have no programming experience.
Because of its popularity, Python has a large community and lots of resources for learning. It also has libraries for machine learning, such as Tensorflow and PyTorch, numerical computing, such as NumPy, and data management, such as Pandas. Because Python can interface with programs written in C++ and C, it is extensible by libraries written in these languages to make it faster. This is how most Python machine-learning libraries are written. This allows your Python code to be performant.
As a result, Python is the most popular language for machine learning and one that you must definitely learn.
JavaScript
JavaScript is one of the most popular programming languages in the world. While it started as a language for scripting webpages, it has since grown into a versatile language used for pretty much anything.
Its uses include writing server-side code, creating desktop and mobile applications, and building machine learning models. The power of JavaScript as a machine-learning language lies in its ubiquity; that is, most devices are capable of running JavaScript code with just a browser.
While most devices do not have the GPUs to run large models, it may still be beneficial to train and use smaller models in the browser. Doing this enables you to build models that train on sensitive user data without needing to send it to the server.
You can also use JavaScript to implement faster predictions. This is because most ML applications send data to the server and return predictions. As a result, the speed of the prediction is slowed down by the latency of the connection, which could create a bad user experience.
You can also create models that run within mobile applications built with React Native. Libraries of machine learning using JavaScript include ML5.js, Synaptic, and Brain.js.
Must-Learn Language for Machine Learning
While all these languages are useful for machine learning, I would say Python is a must-have. In addition to Python, you can learn other languages like Julia or C++ to speed up your code, but the majority of machine learning is done in Python.
So if you want to become a Machine Learning Engineer, you should at least know Python. In addition to the Python language, you should also know NumPy, a Python library for numerical computing.
Also read: Books and Courses to Learn NumPy in a Month
Final Words
Because of its popularity and ecosystem, I do not think Python is going away any time soon. As a result, it is a useful language to learn if you are interested in becoming a machine learning engineer. It is also easier to learn compared to other languages and beginner friendly. It is, therefore, an ideal first language.
After Python, C++ makes sense, as most Python libraries for machine learning are written in C++. This would enable you to work as you can on the libraries themselves and speed up your Python code by extending it in C++. Beyond that, you can pick any other language you choose, such as Julia or R.
Next, check out programming languages to use in Data Science.
