In 2024, a Statista report showed that 49.28% of developers use Python globally. Many attribute this programming language's popularity to its simplicity and readability. What is more, the coding community and educational resources created for Python have made it the go-to choice for beginners and experienced practitioners. With these in mind, it is no longer surprising that it has a rich ecosystem of libraries such as TensorFlow and PyTorch.
However, while Python remains a powerful tool, there are other languages worth exploring when it comes to machine learning — each one serving a specific use case or performance need. These languages can open new possibilities, enhance performance, and provide better integration with various systems and applications.
In this blog post, we will discuss three noteworthy alternatives to Python for machine learning: R, Julia, and JavaScript. By exploring these languages for machine learning, developers can better meet needs of different machine-learning tasks.
R - The Statistical Powerhouse
R is specifically designed for statistical computing and graphics. Ross Ihaka and Robert Gentleman developed R in the early 1990s. As professors at the University of Auckland, they wanted to provide a free and open-source software environment to teach statistics.
Since its creation, R has grown into a powerful tool widely used by statisticians, data analysts, and researchers. R is usually primarily used in academia for research, finance for risk analysis and modeling, and healthcare for biostatistics and clinical trials.
The R Ecosystem
The rich ecosystem of R includes packages like ggplot2 for advanced plotting and dplyr for data manipulation. This is an indicator of how R’s syntax and structure are particularly well-suited for expressing complex statistical models and performing thorough analysis.
To give you a more concrete idea about its ecosystem, here are some of the popular machine-learning libraries written in R:
-
caret (Classification and Regression Training): This is used to simplify the process of model training and evaluation. As a package, caret offers data cleaning, model tuning and comparison, and more.
-
randomForest: Developers use randomForest to consolidate multiple decision trees, or tree diagrams, to get one result. randomForest can also provide the most popular result among many decision trees.
-
xgboost: As an open-source software library, xgboost is used to optimize machine learning. It focuses on performance and efficiency, which is commonly used in competitive machine learning.
Compared to Python, R has a steeper learning curve, which could potentially lower performance in some machine-learning tasks.
But if you want an all-in-one machine-learning solution, R acts as a powerhouse that can offer visualization and data analysis in large volumes.
Julia - The High-Performance Contender
In 2012, Julia was developed by a team of researchers who wanted a free, fast, and high-level language. Their work resulted in a language that combines the speed of traditional languages like C with the ease of use of languages like Python.
They designed Julia to make mathematical and scientific computing fast and easy. This is why it's an excellent choice for machine learning and data analysis tasks.
Julia's speed is because of its Just-In-Time (JIT) compilation using the LLVM framework. The language can handle demanding computational tasks efficiently, making it perfect for complex machine-learning algorithms and large-scale data processing.
Julia Machine Learning Libraries and Applications
As of writing, Julia has comparatively fewer libraries and documentation built around it. Fortunately, there are ongoing initiatives to increase the resources around Julia.
So far, here are some Julia libraries you can explore for machine learning:
-
Flux: A flexible and efficient machine learning library in Julia, known for its simplicity and ease of use. It supports dynamic computational graphs and GPU acceleration.
-
MLJ (Machine Learning in Julia): A comprehensive framework for machine learning that offers tools for model selection, evaluation, and tuning and integrates a wide range of algorithms from various packages.
Despite its limited libraries, Julia is being used in the following high-performance computing applications:
-
Astronomy: Celeste is an astronomical cataloging project that used Julia to process terabytes of data and achieve significant performance improvements. It demonstrates Julia's capability in handling large-scale scientific data.
-
Finance: Firms like BlackRock use Julia for time-series analysis and risk management, leveraging its speed and efficiency in processing complex financial models.
-
Pharmaceuticals: Julia is used for simulations and modeling in drug development, enabling faster computations and more accurate predictions.
Julia's promise lies in its large-scale data capabilities and robust library. As a user-friendly syntax, it can still catch up with its contemporaries. Also, its accessibility and ability to handle large volumes of data can make it an ideal solution for a lot of practical implementations.
With the growing popularity of AI and ML worldwide, the programming language like Python, R, and Julia have made there way to college curriculums, and not just the top engineering schools, but even the cheapest engineering schools.
However, since Julia is not as widely used as other Python alternatives, it can be difficult to integrate it with other languages and tools.
JavaScript - Bringing ML to the Web
JavaScript is a cornerstone of web development. In fact, according to the latest data from Statista, this language is used by 63.61% of developers globally. It runs in the browser, so developers can build responsive user interfaces and handle client-side tasks seamlessly.
In recent years, JavaScript has expanded beyond traditional web development to include robust machine learning capabilities. This growth is fueled by the increasing need to perform machine learning directly in the browser.
Therefore, developers can create systems that enable real-time data processing and interactive AI applications without relying on server-side computation.
Here are some JavaScript libraries used for machine learning:
-
TensorFlow.js: A JavaScript library for training and deploying machine learning models in the browser and on Node.js. It allows developers to leverage the powerful TensorFlow ecosystem in a web-friendly environment.
-
Brain.js: A lightweight library for neural networks in JavaScript, designed to be easy to use. This can be used by developers to integrate machine learning into web applications.
These JavaScript libraries can be applied in these situations:
-
Interactive Web Applications: Through JavaScript, websites can incorporate real-time AI features, such as image recognition, natural language processing, and user behavior prediction, directly in the browser.
-
Personalized User Experience: Machine learning models created with JavaScript can adjust content and recommendations based on user interactions. The result is a customized experience for the end user without server delays.
-
Real-Time Data Processing: JavaScript allows for immediate data analysis and decision-making, which is essential for applications like online gaming, live streaming, and financial trading platforms.
The main positives of JavaScript are that it enables real-time, client-side machine learning with seamless integration into web technologies and is accessible to web developers.
However, it has performance limitations, a less mature library ecosystem than Python, and potential security risks when running models in the browser. Python's better security solution could be a reason why it is part of the IoT technology stack too.
Despite the potential performance and security limitations, its integration with web development and accessibility make it a valuable tool for creating interactive, real-time AI experiences.
Explore Alternative Languages for Machine Learning
In conclusion, R, Julia, and JavaScript are diverse tools that can cater to specific needs and tasks.
R's prowess in statistical analysis and visualization, Julia's high-performance capabilities, and JavaScript's integration with web technologies offer developers a range of options to tackle machine learning challenges.
As you embark on your own machine learning endeavors, remember to consider each language's unique strengths and trade-offs. Do your best to experiment and test to find the best fit for your projects.
