Introduction
Hey there! If you‘re looking to get into the exciting field of machine learning, one of the first things you‘ll need to decide is which programming language to learn.
I‘ve been working in AI for over a decade, both as a machine learning practitioner and engineer. In that time, I‘ve used a wide range of languages for building and deploying ML systems.
In this guide, I‘ll share my hard-earned insights on the top 7 languages used for machine learning today. I‘ll give you the inside scoop on the pros and cons of each language, along with data and examples illustrating when each one shines.
Let‘s start by quickly recapping what machine learning is and why programming languages matter when applying ML. Then we‘ll dive into our list!
What is Machine Learning?
Machine learning is a subset of artificial intelligence focused on building statistical models to make predictions based on data.
The algorithms "learn" by detecting patterns within massive datasets. Once trained, the models can apply what they‘ve learned to make decisions or forecasts for never-before-seen data.
For example, machine learning now powers:
-
Product recommendations – Amazon uses ML to suggest products based on your purchases and browsing history.
-
Fraud detection – Banks employ ML to identify credit card transactions that seem anomalous or suspicious.
-
Text generation – Models like GPT-3 can write human-like text after training on millions of web pages and books.
-
Image recognition – ML can identify objects within photos. Self-driving cars use this to detect pedestrians, traffic lights, and more.
The field relies heavily on statistics, linear algebra, and calculus. But to actually build and apply ML requires coding skills. Which brings us to…
Why Programming Language Matters
The choice of programming language influences multiple aspects of a machine learning project:
-
Training Speed: Some languages execute code faster, which speeds up model training. This difference really adds up for deep neural networks that can take weeks to train!
-
Productivity: Languages with rich ML libraries allow you to do more with less code. They provide abstractions that simplify ML development.
-
Deployment: Low-level languages make it easier to deploy models on constrained devices. Web languages help put models into production within browser-based apps.
-
Learning Curve: Utilizing languages aligned to your background allows you to get up and running faster. Data scientists already know Python or R. Software engineers may prefer Java or C++.
There‘s no universally superior choice. The right language depends on your goals, available resources, and existing skills.
Now let‘s dive into our list of the top 7 by category!
Low-Level Languages
Low-level languages provide minimal abstraction and expose details like hardware management. This allows for faster execution and tighter control – critical in performance-sensitive ML applications.
C and C++ are among the most commonly used low-level choices. But today we‘ll focus on C++ and R.
C++
Background: C++ dates back to 1979 when it was conceived as an extension of C for object-oriented programming. C++ is considered mid-level, sitting between low-level C and high-level languages.
Key Features:
- Compiled language generates extremely fast executing programs
- Static typing catches more errors before runtime
- Fine-grained memory control and hardware access
- Massive libraries with pre-built functionality
Benefits for ML:
- Blazing speed to manage expensive ML model training
- Static typing improves debugging
- Direct hardware control to tune performance
- Leverages optimized math libraries like BLAS
Downsides for ML:
- More complex than high-level languages
- Tricky to manage and debug large projects
- Less abstraction necessitates more code
Popular ML Libraries:
- TensorFlow – End-to-end platform for ML development
- CUDA – APIs for GPU acceleration
- Caffe – Deep learning framework created by Berkeley AI Research
When to Use: For maximum efficiency in computationally intensive applications like training enormous neural networks. Also vital for deploying models in constrained native environments like autonomous vehicles, robots, and IoT devices.
Let‘s move on to arguably the most popular language for data science…
R
Background: R originated in the 1990s at the University of Auckland as a language tailored for statistical computing and graphics. It is an open-source implementation of the earlier S programming language.
Key Features:
- Dynamically typed for interactive data analysis
- Built-in data structures like vectors, matrices, arrays
- Massive library for data wrangling, modeling, visualization
Benefits for ML:
- Purpose-built for math, stats, and data manipulation required in ML
- Simple syntax enables rapid prototyping
- 10,000+ packages providing turnkey ML algorithms
- REPL allows for easy experimentation
Downsides for ML:
- Not as performant as static languages
- Weak software engineering tooling
- Challenging to productize R-based systems
Popular ML Packages:
- caret – Unified framework for tasks like classification and regression
- randomForest – Efficient implementation of random forest models
- e1071 – Tools for SVMs, naive Bayes, cluster analysis, and more
When to Use: R shines during data exploration, analysis, and rapid prototyping of ML techniques. It‘s ubiquitous within the data science community.
R is beginner-friendly but not ideal for operationalizing models. For that, let‘s look at middle-level languages.
Middle-Level Languages
Middle-level languages offer a balance between the raw performance of low-level languages and the ease of use of high-level ones. They provide abstraction while retaining access to hardware capabilities.
Julia
Background: Julia was created in 2009 by researchers who wanted a language as easy as Python and R but fast like C. It reached version 1.0 in 2018.
Key Features:
- Dynamic typing with optional static type annotations
- Designed for high-performance numerical computing
- Multiple dispatch for expressive code
- Seamless Python and R integration
Benefits for ML:
- Near C-speed thanks to just-in-time (JIT) compiler
- Conciseness allows ML models with less code
- Gradual typing system prevents bugs but allows flexibility
- Built for parallelism and distributed computing
Downsides for ML:
- Still a relatively young language
- Smaller ecosystem than more established choices
- Limited production use cases so far
Popular ML Libraries:
- Flux – Deep learning library for Julia
- MLJ – All-purpose machine learning framework
- ScikitLearn.jl – Julia implementation of Python‘s scikit-learn
When to Use: Julia hits the sweet spot for numerically intensive ML when performance matters but ease of use is still important. It‘s a promising candidate to eventually supplement or even replace Python and R.
Now let‘s examine two leading high-level languages for machine learning.
High-Level Languages
High-level languages excel at improving programmer productivity by minimizing hardware involvement. They handle memory management automatically and provide high-level data structures.
This simplifies development but can result in slower execution speeds. Two of the top options are Python and JavaScript.
Python
Background: Python was conceived in the late 1980s by Guido van Rossum as a general-purpose scripting language. It skyrocketed in popularity within the data science and ML community over the past decade.
Key Features:
- Interpreted language with dynamic typing and automatic memory management
- Simple, readable syntax enabling programmers to express ideas concisely
- Vast module ecosystem for everything from web dev to quantitative analysis
- Interoperability with other languages like C, C++, and R
Benefits for ML:
- Massive ecosystem of data science and ML libraries: NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, and more
- Improved productivity from dynamic typing and high-level abstractions
- Readable code facilitates exploration, collaboration, and maintenance
- Easy to learn even for non-programmers
Downsides for ML:
- As an interpreted language, can be slow for large data workloads
- Harder to deploy standalone Python ML systems
- Dynamic typing enables bugs to go unnoticed until runtime
When to Use: Python can be utilized for virtually every stage of the ML workflow including data cleaning, feature engineering, model development, evaluation, and deployment. It is the undisputed #1 language for both ML research and production use.
JavaScript
Background: Created in 1995 to add scripting capabilities to web pages, JavaScript has expanded into a versatile general-purpose language. It is unrelated to Java.
Key Features:
- Prototype-based scripting language with dynamic typing
- Runs natively in web browsers enabling rich UIs
- Asynchronous, event-driven architecture
- Can also be run on servers and supports extensions
Benefits for ML:
- Ubiquitous – runs on any device with a standards-compliant browser
- Enables client-side inferencing and ML applications
- Massive web developer community proficient in JavaScript
- Facilitates interactive model exploration and visualization
Downsides for ML:
- Not well-suited for complex statistical modeling or training
- Browser environment too constrained for most ML
- Still maturing ecosystem of ML-focused tools
Popular ML Libraries:
- TensorFlow.js – Browser-based library for ML and linear algebra
- brain.js – Neural network library
- ml5.js – Wrapper for TensorFlow.js focused on creative ML
When to Use: JavaScript shines for deploying trained models into interactive web apps. It democratizes ML by enabling client-side inferencing using just a browser.
The Right Language for You
We‘ve covered the 7 most popular programming languages for machine learning today. But there are many additional options like MATLAB, Java, Scala, Octave, and more.
There‘s no universally superior choice when it comes to ML languages. It depends entirely on your specific goals and constraints.
Here are a few overarching guidelines:
-
Python – The ideal starting point and the most versatile ML language overall.
-
R – Leading choice for statistical analysis and ML research. Ubiquitous in data science.
-
C/C++ – When you need to squeeze the absolute best performance out of your models.
-
JavaScript – For deploying ML models into interactive web experiences.
-
Julia – Emerging alternative to Python/R for numerical computing.
But it‘s not just about picking one language. Being fluent in multiple languages unlocks new capabilities.
You might use Python for rapid prototyping, then re-implement with C++ for increased efficiency. Or utilize JavaScript to put your Python models into web apps.
My advice is to thoroughly learn the core machine learning concepts first. This will provide the foundation to then leverage programming languages as tools to fulfill your objectives.
Don‘t get distracted up front worrying about the "best" language. Prioritize building ML expertise – the languages will fall into place based on your needs.
I hope this guide has shed some light on the pros and cons of the top programming languages used for machine learning today. Let me know if you have any other questions!
