Finding the best language for machine learning is undoubtedly difficult because over 700 different programming languages for machine learning are widely used.

The best programming languages for machine learning and other languages that machine learning engineers use will be covered in this guide.

The Best Programming Languages to Learn for Backend Development

Machine learning applications exist for many programming languages, including statistical computing, scientific computing, predictive modeling, and artificial intelligence.

Several programming languages can be used to build machine-learning models. We have outlined the best language for machine learning:

1.) Native iOS Swift

For iOS, iPadOS, macOS, tvOS, and watchOS, Swift is a potent and user-friendly programming language for machine learning.

Swift includes contemporary features that machine learning developers adore , and writing Swift code is interactive and enjoyable. The simple syntax is short but expressive.

The most recent findings in machine learning programming language research, along with years of expertise developing Apple platforms, led to the creation of Swift.

Even simple built-in terms that specify asynchronous behavior can be used to build concurrent code, making it easier to comprehend and less prone to errors.

If you’re looking for a relatively easy-to-use coding system that doesn’t require a steep learning curve, try using Native iOS Swift.

PROS

• Rapid machine learning language development

• Scaling the team and the product is simpler

• Enhancements to performance, development speed, and safety

• Reduced memory footprint

• Swift is open-source

CONS

• Language for machine learning is still in its infancy.

• A small talent pool

• Compatibility issues

NOTE : Fortran is the oldest programming language. Lisp is the second-oldest programming language still in common use.

2.) Native Android Kotlin

A solution called Kotlin/Native allows Kotlin code to be converted into native binaries that can operate without a virtual machine.

An LLVM-based backend for the Kotlin compiler and a native implementation of the Kotlin standard library are included in Kotlin/Native.

Kotlin/Native is primarily made to enable compilation for machine learning algorithms like embedded devices or iOS, where virtual machines are either undesirable or impractical.

If you are a new developer who wants easier access to the mobile world, try using Native Android Kotlin.

PROS

• Increasing Team Productivity

• Shortened development period

• Improved Error Messages

• More Concise Kotlin

• Appropriate for Java and is object-oriented

CONS

• Not the same as Java

• Less Rapid Compilation

• Lower Demand for Talent

NOTE : Java is regarded as the programming language appropriate for financial analysis, network security, preventing cyberattacks, and fraud detection algorithms by internal development teams of numerous organizations and financial institutions.

3.) Flutter

Using Google’s Flutter open-source framework, a single codebase may create stunning, natively built, cross-platform applications.

Flutter is an open-source programming language that will help you create beautiful, quick machine-learning apps with a productive, extensible, and open development model.

This machine learning program allows designers to express their creative vision entirely, unrestricted by the constraints of the underlying framework.

Because of Flutter’s sophisticated compositing capabilities and layered design, you have complete control over every pixel on the screen.

PROS

• This machine learning program allows you to make changes to the app immediately.

• Flutter-based apps have excellent UX because of their incredibly smooth speed.

• Testing and quality assurance are typically much quicker with a single code base.

• In Flutter, development happens quickly and effectively.

CONS

• Browsers do not currently support machine learning applications built using Flutter. So, no web apps.

• Flutter has not been around long enough to generate a substantial resource base.

4.) React Native

React, a top-notch JavaScript toolkit for creating user interfaces, combines the greatest aspects of native programming in React Native.

You may already use React Native in your existing Android and iOS applications or start from scratch and build a whole new app.

Facebook developed the open-source React Native technology for mobile applications.

It gives developers complete access to the native platform and native UI features when creating machine learning apps for platforms including Android, iOS, Web, etc.

PROS

• Cost and speed of app development

• There are smartphone apps that use React Native.

• Simple to work with

• Keep things simple and admirable.

• A sizable developer community that consistently makes contributions.

CONS

• Ineffective for complicated interfaces

• Absence of a few custom modules

• There might be some updating problems.

5.) React.js

React JS is a library with features of a machine learning model, so it’s okay if you accidentally refer to it that way occasionally or if you come across an article that does.

However, based on the rankings, we can confidently say that despite being unique, React is still valued for what it is.

Many developers choose React as their first choice when creating web apps since it is a great library.

React’s growth may seem overwhelming to some, but that doesn’t change the fact that this JavaScript library is still favored by JavaScript developers worldwide.

PROS

• Simple to use and learn

• It gets simpler to create dynamic web applications.

• Reusable components

• Improvements in performance

• The use of practical tools

CONS

• The high pace of development

• Inadequate documentation

6.) Node.js

Node.js is a cross-platform, open-source runtime environment for server-side and networking programs. Compared to more established web application frameworks, it has several benefits.

Enterprises utilize Node.js to offer web services, which run some of the most popular websites on the internet, like Facebook and LinkedIn.

This machine learning program is designed to construct quick and scalable network apps and is built on Chrome’s JavaScript runtime.

This JavaScript runtime is ideal for data-intensive real-time applications that operate across dispersed devices because it employs an event-driven, non-blocking I/O approach.

PROS

• It provides a simple scalability

• Simple to learn

• Renowned for providing high performance

• Large and active community support

• The benefits of caching

CONS

• API instability

• Has a weak support system for libraries

• Unable to handle CPU-intensive tasks as well

7.) Nest.js

NestJS is a framework that employs the appropriate architectural strategies and establishes rules to simplify the developer’s life.

In this machine learning program, you can use the full platform or its components because everything is packed in a straightforward and lightweight manner.

Before then, development on NestJS on node was done just for fun, and an attempt was made to include typescript and best practices into the well-liked Koa.

It is regarded as the beginning of a trip into the world of cutting-edge web application design theories, including microservice architectures, event sourcing, and domain-driven design.

PROS

• Simple API endpoints are very simple to write.

• Boilerplate code generation and integration CLI tool.

• Extensible: NestJS is adaptable and lets you use different libraries in your project.

• The project architecture of NestJS enables testing, scaling, and maintenance functions with a minimum of work.

• A versatile machine learning program

CONS

• Steep learning curve

• Magic in excess. Obfuscation and magic are similar in that things function until they don’t.

• Lacking functionality compared to frameworks in other machine learning languages

8.) Laravel

Laravel is a web application framework that simplifies web development with its expressive and elegant syntax.

Due to its many built-in capabilities and the fact that it makes web development simple, such as through the use of a modular packaging system, Laravel is well-liked among web developers.

Among other things, a comprehensive authentication system and automatic testing machine learning techniques exist.

Laravel is a flexible framework for various machine-learning projects, including caching and routing. It is primarily a backend development framework but also has front-end features.

PROS

• Easy coding

• One such scalable framework that you may use for small and medium-sized web applications is Laravel.

• There is a secure access control system built into the software.

• Data migration is simple

• Simple to learn

CONS

• It falls under the category of lightweight software and has fewer functions.

• High price

• Limited assistance

9.) Elastic Search

Elasticsearch is a real-time, distributed, open-source restful search and analytics engine built on Lucene. Within one second, quick searches are completed, and results are returned.

Several machine learning programming languages, including Java,.Net, Groovy, and Python, are used to create clients for Elasticsearch.

Web search, application search, logging data analytics, application performance monitoring, and geospatial analysis are a few machine learning models of the use cases.

PROS

• Because Elasticsearch was created in Java, it can run on any platform.

• Being a real-time search engine, it can only be searched for one second after a document is added.

• Elasticsearch provides the gateway idea, which makes it simple to create comprehensive backups.

• Because it is distributed document-oriented, scaling up in big organizations is simple. The developer can quickly integrate it into any sizable organization by scaling it.

• In contrast to Apache Solr, Elasticsearch can handle multi-tenancy with ease.

CONS

• Split-brain issues can occasionally arise in Elasticsearch.

• Elasticsearch lacks the multi-language capability for managing request and response data, in contrast to Apache Solr.

• As opposed to alternatives like MongoDB, Hadoop, etc., Elasticsearch is a poor choice for a data store. Streaming TBs of data daily either chokes or loses the data; however, it works fine for smaller use cases.

10.) Python

Python is the most used general-purpose programming language for sentiment data analysis and natural language processing (NLP).

The burgeoning fields of data science, artificial intelligence, and machine learning can benefit from this language. A gold standard for those interested in scientific computing and ML.

Python is frequently used by machine learning engineers and data scientists, even though 33% of them prioritize using it for development.

This object-oriented programming language is a good example of why this flexible machine-learning language is a cross-platform language.

PROS

• Better for data manipulation and repetitive tasks

• A sizable community

• Extensible and object-oriented

• Numerous Libraries

• Embeddable

CONS

• Problems of design

• Sluggish compared to compiled machine-learning languages

• High memory requirements

NOTE : Algorithms can learn to comprehend the subtleties of human language through natural language processing (NLP). Visualizing web-based dashboards and random number generation are made possible using ML algorithms built-in JavaScript.

What Is Machine Learning?

Making it possible for computers to learn from data is the goal of the rapidly growing data science subject known as machine learning. It deals with the statistical analysis of data.

The main objective of machine learning is to develop operating systems that can recognize patterns, gather knowledge from data, construct decision trees, and draw conclusions.

There are numerous uses for machine learning, including computer vision, neural networks, predictive data analytics, and natural language processing.

A programmer doesn’t write the code that tells machine learning algorithms how to distinguish between an image of a cat and a dog in machine learning.

Instead, by training on large data samples, machine learning models are created that teach how to distinguish between a dog and a cat.

One needs a solid foundation in programming, algorithms, data structures, memory management, and logic to implement ML models.

Artificial intelligence can be categorized as either machine learning or deep learning. Machine learning, in essence, is AI that can autonomously adapt with little assistance from humans.

Artificial neural networks are used in deep learning, a subset of machine learning, to simulate how the human brain learns.

Through a high-level programming interface, the deep learning framework provides the building blocks for creating, training data, and verifying deep neural networks.

** TIP ** : Without a solid foundation in statistics, deep learning, computer systems process, design, etc., you’ll never be able to select the best models or address ML issues.

How Can You Learn Machine Learning?

Data analysis, data mining, data organization, and test and experiment execution are all part of machine learning.

Machine learning processes are typically tracked and improved to create reliable and powerful machine learning systems.

The five steps for learning “machine learning” are listed below so that you can start your new career in this fascinating field.

1) Develop Your Programming Skills

For a profession in machine learning, expertise in various programming languages is required.

Knowledge of one or two of the top programming languages for machine learning is required to complete various projects.

Numerous internet resources exist to help machine-learning practitioners choose a machine-learning programming language to study.

Python is an object-oriented programming language for machine learning that’s most often used by many machine learning engineers and data scientists.

2) Take a Course in Machine Learning

One of the highest-paying careers without a college degree is machine learning. But you still need to finish a boot camp or online course.

Machine learning practitioners can quickly learn and comprehend machine learning thanks to the many well-regarded bootcamps and programs available.

3) Begin Initiatives Utilizing Machine Learning

Examine and build straightforward machine learning projects from Scikit-learn and other corresponding sources to better understand machine learning.

You will have a basic understanding of machine learning after completing this task. You can produce original works that you can publish online or list on your resume.

Try starting from scratch on a machine-learning project that calls for straightforward machine-learning algorithms.

4) Study Data Collection Techniques

Data gathering is crucial to machine learning. You can gather the required statistical data effectively and efficiently if you are familiar with various data collection methodologies.

Machine learning systems can complete machine learning tasks quickly and easily when given the right data.

5) Apply for Internships and Apprenticeships in Machine Learning

To develop the business-specific machine learning skills that many organizations desire, you will need the necessary work experience.

A career in machine learning can be launched successfully through an apprenticeship. One way to gain experience is through internships or entry-level positions in machine learning.

Additionally, you can apply for a junior machine learning engineer position in online job postings.

What to Consider When Choosing the Best Machine Learning Programming Languages

The following are the seven important variables that we take into account while choosing the best language for machine learning for each project:

1) Application Type

What languages are available to us depends greatly on the type of application you’re developing, including web applications, mobile applications, embedded firmware, etc.

A variety of machine learning applications can be created using general-purpose programming languages like Java, JavaScript, Python, and C# on various platforms.

In some circumstances, a particular machine-learning language performs better.

2) The Application’s Complexity

The scope and difficulty of a project are crucial factors in selecting the stack or technology we should employ, affecting the choice of machine learning programming language.

A more general-purpose programming language, like Java or C#, could help simplify the skill set requirements to maintain the products.

Medium-sized projects, such as e-commerce sites, internal enterprise applications, and IoT solutions, typically have various layers, components, and integrations.

3) Business Culture

Every business prefers internal versus external technical support and open source versus proprietary software.

A more scalable machine-learning programming language might be required by one company, while another might prefer an easy-to-learn machine-learning language.

All programming languages have trade-offs, so our main goal is to comprehend your unique business challenges before choosing a machine-learning language that suits them.

4) Time to Market

It’s crucial to consider your current employees’ skill sets. Is it possible to teach your staff new software or technology?

We usually advise leveraging current skill sets to bring new ideas to life more quickly unless there is a compelling reason to introduce new technology.

It is best to reserve a language for machine learning and technology exploration for tasks with lengthier deadlines.

The physical environment you currently operate in or may want to be in also impacts time to market.

5) Maintainability

Every language for machine learning and technology stack has its ecosystem of machine learning libraries and vendor support for those machine learning libraries.

We want to consider the trend and update release cycles to ensure that our choice is current and will remain for some time.

Even though we like to work with our clients over the long term, we are aware that sometimes it may be necessary to hand off a codebase to another team.

When choosing your preferred programming language, it is considered to ensure the codebase is long-lasting.

6) Scalability and Effectiveness

Instead of the machine learning programming language, scalability is more about a technology stack influencing the programming languages.

Some well-known and tried-and-true stacks demonstrating their capacity for such scalability include Java Spring,.NET, LAMP, Ruby on Rails (RoR), MEAN, and Ruby on Rails (RoR).

7) Security

Security requirements vary depending on machine learning applications.

Java is the most appropriate programming language for financial analysis, network security, thwarting cyberattacks, and fraud detection techniques.

Micro-service architectures are being used more frequently so that we can execute different services created using other programming languages and frameworks.

Frequently Asked Questions (FAQs)

We’ve answered frequently asked questions about programming languages for machine learning and other crucial subjects.

What Are the Most Popular Programming Languages?

The most popular programming languages in the world are JavaScript (69.7%), followed by HTML/CSS (62.4%), SQL (56.9%), Python (41.6%), and Java (38.4%).

Additionally, hiring managers in the Americas find Java the most popular programming language (PDF, 2.4 MB).

What’s the Importance of Learning a Programming Language?

By learning computer programming, students can gain entry into the dynamic, creative world that depends on machine connections.

These abilities are transferable to so many various fields and industries. A creative career path for students can include 3D animation, web design, or even branding.

What Are Machine Learning Engineers?

Machine learning engineers use their coding and programming talents to gather, process, and analyze data.

They are a hybrid of software engineers and data analysts. To assist in organizing data, machine learning engineers develop predictive models and algorithms using machine learning.

What Is a Java Virtual Machine Used For?

Java bytecode is loaded, validated, and executed by the Java Virtual Machine, or JVM.

Because it executes Java code, it is referred to as the interpreter or the core of the Java programming language.

What Does an Embedded Computing Hardware Engineer Do?

Engineering for embedded systems includes design, development, production, testing, and maintenance.

Regularly tests systems to weed out potential problems and offers system-level support while working with cross-functional teams.

What Is a Neural Network Library?

A deep learning framework called Neural Network Libraries is designed to be used for both study and production.

We plan to run it on desktop computers, HPC clusters, embedded devices, and production servers, among other things.

Final Words

This blog post about the best language for machine learning perhaps helped you learn something.

Machine learning is in an exciting phase right now, regardless of the language for machine learning you pick.

A machine-learning expert can select the best language for a machine-learning using experimentation, testing, and experience.

Knowing a particular programming language for machine learning is ideal because it will help you stand out from the competition while applying for jobs.

Understanding another machine-learning language is simple once you become proficient in one.