Most Popular Programming Languages & Why They’re Useful in Machine Learning

Online forums and data science blogs have plenty of recommendations: learn that language, keep up with that framework, have you looked at that library? It can be hard to keep up with all the latest technologies, especially in machine learning, a field that changes almost daily. 

In this article, we will take it back to the basics, and discuss the most useful programming languages for machine learning. In addition to going over a brief summary of how these languages work, we will cover the basics of machine learning, popularity trends among data scientists, useful libraries, use cases, and paradigms. Let’s dive in!

Basic overview of building a Machine Learning model 

Before learning why certain programming languages are better suited to ML, it is important to understand the basics of building a ML model. 

Machine learning is the closest thing to mimicking the human brain. ML algorithms search for patterns in swaths of data – images, numbers or words – in order to make predictions. Underneath the hood of search engines and content recommendation systems are these powerful machine learning algorithms. 

The are 4 basic steps when you build a machine learning model: 

Step 1: Putting together a dataset 

The dataset must reflect real life predictions that the model will make. Training data can be sorted into classes; the model will pick out different features and patterns between the classes to learn how to tell them apart. For example, if you train a model to sort zebras and giraffes, the dataset will have images of the two animals, appropriately labelled. 

You want to prepare an all inclusive dataset, so the model’s predictions won’t be inaccurate or biased. The dataset should be randomized, deduped, comprehensive, and split into training and testing sets. 

You use the training set to train the model, and the test set to determine the accuracy of the model and identify potential caveats and improvements. The training and test sets shouldn’t have overlapping data. 

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Step 2: Choosing an appropriate algorithm 

Depending on the task at hand, amount of training data, and whether the data is labeled or unlabeled, you use a specific algorithm. 

Common algorithms for labeled data include: 

• Regression algorithms 
• Decision trees
• Instance-based algorithms

Common algorithms for unlabeled data include: 

• Neural networks

• Association algorithms

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Step 3: Training the algorithm on the dataset 

The model trains on the dataset over and over, adjusting weights and biases based on incorrect outputs. For example, if we have the equation for a straight line: y = mx + b, the adjustable values for training are ‘m’ and ‘b’, or our weights and biases. We cannot impact the input (y) or output (x), so we would have to play around with m (the slope) and b (y-intercept) During the training process, random values are assigned to m and b, until the position of the line is affected in such a way that it will have the most correct predictions. As it continues to iterate, the model’s accuracy keeps increasing. 

Step 4: Testing + improving the model 

You can check the model’s accuracy by testing, or evaluating, it on new data that has never been used for training before. This will help you understand how your ML model performs in the real world. After evaluation, you can fine tune hyperparameters that we may have originally assumed in the training process; adjusting these hyperparameters can become somewhat of an experimental process that varies depending on the specifics of your dataset, model, and training process. 

Applications of Machine Learning 

Now that we learned the theory behind constructing a simple machine learning model, let’s explore various applications in the real world. This is what you will be able to build once you learn machine learning!

Recommendation Engines

Almost every major platform has recommendation systems. They collect user data to suggest products, services, or information. Recommendation systems might track data such as the user’s viewing history, how long the user views something, how they react to it, etc. 

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Social media 

Social media platforms use a variety of ML tools to keep their uses hooked. For example, Facebook analyzes what content you like in order to provide relevant advertising. Instagram identifies visuals with image processing. Snapchat tracks your face movement while placing a filter over it using computer vision. 

Self-driving cars

In order to avoid nearby objects such as pedestrians and other cars, self-driving cars collect data from their surroundings via sensors and cameras. Using ML algorithms such as SIFT (scale-invariant feature transform), the cars can behave as if someone is actually behind the wheel. 

Education 

ML can be used to identify struggling students or gaps in educational platforms. Flashcard tools such as Anki and Quizlet use algorithms to track memorization and retention rates.

Medicine + Health 

ML technologies are becoming a prominent part of the healthcare industry. ML algorithms can be used to determine the best course of treatment, help with more accurate diagnosis and drug development, and much more. Healthcare administrative systems use ML to map and treat infectious diseases, and provide individualized care to patients.

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People detection 

People detection lets you identify and track people through a camera or in a video. This technology is used in security devices, and most recently being implemented in the Amazon Go Grocery Store, which tracks customers as they shop so they don’t have to checkout. 

Programming languages for ML

You now have a basic understanding of ML and its real-world applications, but it might be difficult to know where or how to start. A great first step is getting to know at least one of the main programming languages used in machine learning.

Before we dive in, let’s talk about popularity among data scientists. The Developer Survey in 2018 conducted by Stack Overflow revealed that Python was the most popular programming language, followed by Java and Javascript. 

Python

Python has undisputed popularity. Guido Van Rossum, Python’s creator, says “I certainly didn’t set out to create a language that was intended for mass consumption.” Safe to say, he has done the opposite, as Python brings coding to the forefront for people who shy away from it – long gone is overly complex syntax. 

This graph that depicts popular Python use cases is another product of the Developer Survey. Initially, web development seems like the most popular use of Python, with about 26%. However, the combination of data analysis and machine learning reveals a staggering 27%. So why is Python so popular for ML? 

Before we dive in, we have to understand that AI projects are different from traditional software projects in terms of the technology stack and skills required. Therefore, choosing a programming language that is stable, flexible, and has a diverse set of tools is so important. Python meets all of these. 

In addition to simplicity and consistency, it has a great community that helps build a variety of ML frameworks and libraries. These are packages of pre-written Python code that help machine learning engineers quickly solve common tasks and develop products much quicker. 

As you can see, Python provides an easy experience for ML that no other language quite can: you don’t have to reinvent the wheel here. 

Python is more intuitive than other programming languages because it has dead-simple syntax and is the best option for team implementation. Developers can focus on the ML task at hand such as complicated algorithms or versatile workflows, rather than the specifics of the language. Just take a look at this simple example: 

C:

#include<stdio.h>
#include<conio.h>
main()
{
    printf("Hello World");
}

Java: 

public class HelloWorld {
   public static void main(String[] args) {
      System.out.println("Hello, World");
   }
}

Python is also incredibly flexible and platform-independent, as it’s supported by Linux, Windows, and macOS without the need for a Python interpreter. This also makes training a lot cheaper and simpler when using your own GPUs. 57% of machine learning engineers report using Python, and 33% of them prefer it for development. 

However, as for anything, we have to take into account the disadvantages of Python as well: 

• Has little to none statistical model packages
• Threading in Python is quite problematic because of the Global Interpreter Lock (GIL), and multi-threaded CPU-bound applications run slower than single-thread ones. 

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R

Now let’s look into R. R was built for high-level statistics and data visualization. For anyone who wants to understand the mathematical computations involved in machine learning or statistics, this is the language for you. 

R beats in Python in terms of data analysis and visualization. It allows for rapid prototyping and work with datasets in order to build your ML models. For example, if you would like to break down huge paragraphs into words or phrases to look for patterns, R would beat Python. 

R also comes with an impressive collection of libraries and tools to help with your machine learning pursuits. These advanced data analysis packages cover both the pre- and post- modeling stages, and are made for specific tasks such as model validation or data visualization. 

Helpful packages and libraries include:

In addition to an active and helpful open source community, R is free to download and comes with GNU packages, placing it among expensive alternatives such as SAS and Matlab. R Studio is an IDE that lets developers create statistical visualizations of ML algorithms. R comes with a console, syntax highlighting editors, and other useful tools such as plotting, history, debugging, workspace management. 

To get started with R and download R studio, check out its website.

Now let’s talk about the not-so-fun part. The disadvantages of R include: 

• A steep learning curve: R is a hard language, potentially making it harder to find experts for a project or team. Any new package that you use will need learning, and there is no thorough documentation of R. 
• R can be inconsistent, as its algorithms are from third parties. 

The big question is often R or Python when it comes to ML. Both of them have their advantages and disadvantages, but Python is better for data manipulation and repetitive tasks. If you would like to build some sort of product that uses ML, go with Python. If you need some in depth analysis, R is your best bet. 

Julia

We have talked quite in depth about the two behemoth languages for ML now. But Julia is a real underdog: although not as popular as Python and R, it was made to match the functionality of Python, MATLAB, and R, along with the execution speed of C++ and Java. Now that’s reason enough to keep it in mind! Java has two huge advantages: speed + designed for parallelism.  Because it feels like a scripting language, it’s also not difficult to switch to, so Python / R developers can pick it up easily. 

In terms of AI, Julia is best for deep learning (after Python), and is great for quickly executing basic math and science. Julia focuses on the scientific computing domain and is greatly suited for it. Because of these computing capabilities, Julia is scalable and faster than Python and R. 

Its powerful native tools include: 

Julia can also call Python, C, and Fortran libraries, and comes with an interactive command line and a full-featured debugger. 

However, compared to Python, Julia lacks in terms of object oriented programming, scalability, community, and variety of libraries. It’s still in its infancy. Most ML experts use both: Julia for the backend deep learning, where it achieves the best performance rates, and Python for the front end. 

JavaScript

When you think of ML, JavaScript certainly isn’t the first in mind. Although JavaScript is primarily used for web development, it has slipped into machine learning with TensorFlow.js. TensorFlow.js is an open-source library created by Google that uses JavaScript to build machine learning models in the browser, or in Node.js, JavaScript. TensorFlow.js is a great entry into ML for those only familiar with web development. TensorFlow.js supports WebGL, so your ML models can operate when a GPU is present; for example, if users open the webpage on their phones, the model can utilize sensory data. Tensorflow.js lets you import existing, pre-trained models, retrain an imported model and create models within the browser. Let’s look at the pros and cons of TensorFlow.js

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A lot of developers are taking ML from back-end servers to front-end applications. TensorFlow.js allows developers to now create and run ML models in pure HTML without complicated backend systems. This simplicity lets you make great projects easily. Here are a few examples: 

• Automatic Picture manipulation: generate art via convolutional neural networks
• Games using Ai
• Content recommendation engines
• Activity monitoring that learns usage patterns on a local network/ device
• Object detection, for example to identify a license in a photo

Scala 

Scala is significantly faster than Python, and brings the best of object oriented and functional programming to one high-level language. It was originally built for the Java Virtual Machine (JVM) and is very easy to interact with Java Code. Developers can easily build high-performance systems, while avoiding bugs through Scala’s use of static types. 

Scala has several libraries that work for linear algebra, random number generation, scientific computing, etc.: 

Scala is also a great choice for Apache Spark in terms of performance, learning curve, and ease of use (Apache Spark is a data processing framework for processing tasks on giant data sets and distributing data processing tasks through several computers). 

Now let’s compare the pros and cons of Scala: 

C/C++

C/C++ and machine learning is a difficult pairing. From the get go, it seems like Python has many advantages over C/C++:

• Python is more flexible, has a simple syntax, and is easier to learn
• Using Python lets you focus on the nuances of ML, rather than the language
• Tons of libraries and packages
• You can work interactively with data just through the command line via the Python interpreter
• Debugging in C/C++ for ML algorithms is much harder

However, there are also advantages to using C/C++: 

• C/C++ is one of the most efficient languages – and machine learning algorithms need to be fast. 
• Using C/C++ lets you control single resources starting from memory, CPU,etc. 
• A lot of ML frameworks such as TensorFlow, caffe, vowpal, wabbit, libsvm, etc. are actually implemented in C++ 
• You will definitely stand out to recruiters and companies

As one of the oldest programming languages, C and C++ are a niche in terms of machine learning. C can be used to complement existing machine learning projects and computer hardware engineers prefer C due to its speed and level of control – you can implement algorithms from scratch using C/C++. 

Generally, use C/C++ when: 

• Speed is extremely important
• There isn’t a Python library for your use case
• You want to control memory usage because you will be pushing your systems limit

Java

Java is typically known for enterprise development and backend systems. However, there are several reasons to choose Java over Python or R:

• A lot of companies’ infrastructure, software, applications, etc. are built with Java, meaning integration and compatibility issues are minimized
• A lot of popular data science frameworks such as Fink, Hadoop, Hive, and Spark are written in Java
• Java can be used for various processes in data science such as cleaning data, data importation and exportation, statistical analysis, deep learning, NLP, and data visualization
• Java Virtual Machine lets developers write code that will be identical across multiple platforms, and also build tools much faster 
• Applications built with Java are easy to scale with 
• Java is speedy and fast executing like C/C++, which is why Linkedin, Facebook, and Twitter use Java for some of their ML needs
• Java is a strong typing programming language, meaning developers must be explicit and specific about variables and types of data
• Production codebases are often written in Java

Java also comes equipped with various tools and libraries for ML as well:

Energy data for all the programming languages  

In 2017, Portugal conducted an interesting research investigation and released “Energy Efficiency Across Programming Languages.” They ran the solutions to 10 problems from the Computer Language Benchmarks Game, a standard set of algorithm problems in 27 different languages, while meticulously recording how much electricity, speed, and memory each used. The results are summarized in this chart: 

As expected, out of all the languages we discussed today, C turned out to be the fastest and most energy efficient. 

Comparison of all the programming languages  

Let’s take one final look at all the various features offered by these languages:

	Python	R	Julia	Javascript	Scala	C/C++	Java
Paradigm	Object oriented, imperative, functional, aspect-oriented, reflective	Mix of object oriented and functional programming, imperative, reflective	Multiple dispatch, therefore easy to express object-oriented and functional programming patterns	Imperative, object-oriented, functional, reflective	Object oriented, functional, generic	Imperative	Imperative, object oriented, generic, reflective
Standardized	No, as the language reference is included with each version’s documentation	No		Yes	Yes	Yes	Yes
Type Safety	Strong	Strong	Strong	Weak	Strong	Weak	Strong
Type Strength	Safe	Safe	Safe		Safe	Unsafe	Safe
Expression of Types	Implicit	Implicit	Implicit	Implicit	Partially implicit	Explicit	Explicit
Type Compatibility		Duck, structural			Nominative, structural	Nominative	Nominative
Type checking	Dynamic	Dynamic	Dynamic	Dynamic	Static	Static	Static
Parameter Passing	By value	Value by need	“Pass-by-sharing”	By value	By value + name	By value/ pointers	By value
Garbage Collection	Yes	Yes	Yes	Yes	Yes	Optional	Yes
Intended/ Mainstream Use	ML, Web dev., Game Dev., Software dev.	Statistical computing and graphics, numerical computation, visualization	Parallelism,//r//n//r//n“We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell.” Source	Web Scripting, Web + Mobile dev, web servers + game dev.	General purpose language, Parallel Computing, DSL and computing	System, embedded	Application, server-side, back end development, Android dev.
Design Goals	Productivity, code readability, simplicity, modularity	Interactive, thorough analysis of datasets, advanced data analysis	General purpose, thorough analysis of datasets, powerful and fast	Web Dev.	Better alternative to Java, concise, type safe, scalable, platform independent	Low level access	Simple, secure, distributed, object oriented, robust, portable “Write Once, Run Anywhere”
Popularity: Prioritized by*	57%	31%	N/A	28%	N/A	43%	41%
Popularity: Used by*	33%	5%	N/A	7%	N/A	19%	16%
Popular Usage*	Sentiment analysis, NLP/chatots, web mining	Sentiment analysis, bioengineering/bioinformatics, fraud detection	Scientific calculations	Search Engines, web dev.	Big Data	AI in games, robot locomotion, network security + cyber attack detection	Customer support management, network security + cyber attack detection + fraud detection
Professional Background before entering ML*	Data science	Data analyst/ statistician	N/A	Front end web developer	N/A	Embedded computing hardware, electronics engineer	Frontend desktop application developer
Reason to get into ML*	Curious about ML	Data science		Increase chances of securing work		Add machine learning to existing apps	Company
Popular Libraries	Keras, Tensorflow, Scikit learn, NumPy, SciPy, Pandas, Seaborn	Tidyr, Ggplot2, Dplyr, Tidyquant	Flux, Knet, MLBase.jl, TensorFlow.jl, ScikitLearn.jl	TensorFlow.js	Saddle, Aerosol, Breeze, Scalalab, NLP	Tensorflow, Microsoft Cognitive Toolkit, Caffe, MLpack, DyNet, Shogun	Weka, Apache mahout, Massive Online Analysis, Deeplearning4j, Mallet
Resources / Documentation / Community	Great	Great	Starting out	Could be better for ML	Could be bigger	Good	Good