The ability of Machine Learning to rapidly and precisely anticipate results in complex networks has made it an indispensable component of modern technology. Therefore, anyone handling large datasets must eventually determine the best language for Machine Learning.
When hiring developers for machine learning, it is vital to evaluate their programming proficiency. For effective deployment of machine learning models, it is crucial that the candidate has a solid understanding of programming basics such as algorithms, data structures, logical reasoning, and memory management, which form the foundation of most programming languages. A good comprehension of these fundamentals is indispensable for achieving the best outcomes in machine learning projects.
Choosing the appropriate language for machine learning is crucial. This article explores some of the top languages for machine learning expected to dominate in 2023.
Table of Contents
- First Impressions on Machine Learning
- It is now 2023; which language would you suggest for machine learning?
- The R Language
- C/C++ Language
- What Is the Most Popular Programming Language for Machine Learning?
- The Future of Machine Learning Is Exceptionally Promising.
First Impressions on Machine Learning
Machine Learning is a subfield of Artificial Intelligence that empowers computer systems to make predictions on uncharted cases independently, eliminating the necessity for manual programming. Machine Learning algorithms achieve this by employing pre-existing output data as input, allowing them to anticipate potential outcomes.
Machine learning libraries are available to simplify the inclusion of machine learning logic in software developed in specific programming languages.
There are a variety of programming languages that can be utilized to create machine learning models. Below is a list of the top ten languages currently used for machine learning.
In 2023, which is the language you would suggest for machine learning?
Python is well-known for its versatility and capability to execute intricate scripts and online applications, making it an excellent choice for programmers of all levels of expertise. Introduced in 1991, it remains a prominently used, multipurpose programming language.
It is greatly appreciated by developers for its simplicity and user-friendliness. It is compatible with various libraries and development environments.
Python is becoming progressively more in-demand among data analysts, machine learning experts, and developers. Its flexibility and user-friendliness have made it a favorite among programmers. Consequently, Python language specialists are in high demand.
Due to their open-source development and scalability, libraries such as sklearn and seaborn are widely accessible and offer critical features. These reliable libraries streamline the programming process, resulting in machine learning models with improved learning capabilities.
Python has the capability to function in various programming paradigms, such as object-oriented, functional, imperative, and procedural. In regards to machine learning frameworks, Python developers tend to prefer both TensorFlow and Scikit.
Python’s potential uses:
- Scientific numerical methods
- Sentiment analysis
- Natural language processing
- Data science
2. The R Language
R is a commonly used open-source language for machine learning and statistical computing, most notably recognized for its emphasis on data visualization. Furthermore, it offers abundant resources for creating machine learning applications.
Due to its user-friendly attributes, R is a preferred option for data mining, data analysis, and statistics. It presents a broad range of tools for efficient management and excellent graphical representation, in addition to a command-line interface and several integrated development environments.
Data scientists in the private sector, notably those working in the healthcare sector, utilize this graphical programming language for analyzing datasets.
R is a robust tool for creating Representational State Machines (RSMs) for various tasks, such as classification, regression, and decision tree construction. It is recognized for its statistical and functional capabilities, making it a highly interactive, imperative, and functional mode of communication. Moreover, it is not restricted to Windows, Linux, and Mac OS X operating systems but is also compatible with many others.
Based on the latest statistics from the Tiobe Index, R is ranked as the 15th most popular programming language. Given the passion of the developer community, finding experienced R programmers to work on your project should be relatively easy.
Weka and Rapid Miner are sturdy frameworks provided by Java to incorporate machine learning techniques, such as decision trees and regression.
For machine learning applications to be successful, these technologies require access to a multitude of machine learning libraries.
For machine learning applications with an emphasis on model analytics, numerous developers choose Julia. This esteemed, high-level, dynamic programming language is recognised for its speed and intuitive syntax, which is why it is favoured by developers.
There are benefits such as:
- Incredible precision of numerical computations
- Precise compilation
- Employing a model of parallel execution with dispersed tasks
- A vast range of mathematical operations.
Scripts created using this language offer the benefit of being cross-platform compatible, resulting in greater interactivity. Its widespread adoption is attributable to its functional and object-oriented disposition, making it an excellent option for crafting software for machine learning.
As intended, the MIT licence makes it open source and free-to-use. Julia is capable of functioning equally well in a server or client environment.
It is also favoured among data analysts and statisticians due to its efficacy in numerical and computational statistics computations.
Lisp, a time-honoured programming language, is experiencing renewed popularity owing to its capacity for AI and machine learning. Its design and techniques are strongly preferred by some developers, particularly in the field of artificial intelligence and machine learning.
John McCarthy, who is widely considered the “father of artificial intelligence”, established Lisp, which boasts numerous benefits. Its application in machine learning has been particularly advantageous due to its capability for facilitating quick development and experimentation.
Given the iterative nature of AI application development, an agile methodology is crucial. Lisp’s iterative development cycles render it a perfect option for this environment since it affords developers with boundless opportunities for customisation, such as list processing and symbolic expression (symbol usage in computation, which is particularly advantageous in machine learning applications).
Automatic garbage collection is gaining popularity because of its capacity for enabling code to operate more efficiently. This has become particularly significant with the growing usage of domain-specific languages (DSL) among developers, property managers, and other stakeholders.
Locating specialist programmers for Lisp can be challenging, resulting in limited adoption of the language. Furthermore, using Lisp entails significant alterations for compatibility with new hardware and software systems, thus making it less appealing than other languages for machine learning applications.
Scala has garnered considerable notice in machine learning circles given its usage of a renowned compiler, resulting in speedy execution of compiled code. Moreover, its static structure facilitates compatibility with myriad extant Java libraries and frameworks.
Scala is well-known for its ability to handle vast databases and offer adaptable solutions for enterprise-level software. Its primary allure is the ease with which it enables the creation of data-intensive, enterprise-level applications.
The prominently employed Apache Spark framework proffers support for the Scala MLLIB library, endowing it with access to potent functionalities. This permits it to effortlessly manage copious amounts of data.
Software engineers can employ Spark and various other big data tools and technologies to build, experiment with, and launch machine learning algorithms. Scala provides libraries, including Aerosol and Saddle, that are ideal for generating random numbers for diverse applications, including scientific computing and linear algebra. These libraries encompass a plethora of utilities that can be employed to manipulate data, such as two-dimensional data structures and auto-alignment tools.
Scala is not typically used as a primary language, but it can be relatively easy for Java developers to learn. For this reason, it is frequently preferable to search for a Java programmer who is open to acquiring new skills, rather than recruit a new resource with the requisite experience.
C and C++ are widely known as two of the most potent, versatile and extensively-used programming languages globally. In terms of machine learning algorithm development, these languages are unrivalled.
Thanks to their regular updates, languages like C, C++, Java and Python have held sway over the developer community for a considerable period.
These languages are low-level, making them easily understood and interpreted by computers. With hardware-level capabilities being readily available, Machine Learning applications are a viable option for Internet of Things (IoT) devices.
The ability to swiftly develop and launch applications like this is highly advantageous. Libraries like Torch, TensorFlow, etc. have been integrated with C/C++ and have demonstrated their usefulness for resource-intensive software.
C++ is acknowledged as a prime language for modifying algorithms and for executing rigorous memory management. Its usage empowers programmers to precisely adjust numerous performance parameters.
Go (Golang) is a widely-used programming language, renowned for its open-source status, Google ownership, and fast processing speed. It is ideal for managing large datasets because of its ability to execute multiple processes simultaneously.
One of the main benefits of Go is its capacity to operate simultaneously with other processes. It is also classified as a system-level language because of its inherent vocabulary.
With substantial support from leading cloud providers, Go has quickly become one of GitHub’s most sought-after languages. Thanks to its likeness to C, garbage collection, and dynamic typing, it is extensively employed in serverless computing architecture.
Go is a relatively easy language to learn, thanks to its uncluttered syntax and in-built safety measures. Furthermore, companies are eager to recruit experienced Go engineers.
Shell is a programming language inspired by the Unix shell, which is a command-line interpreter.
Because of its simple syntax, Shell is an excellent option for developing machine learning tools, algorithms, and applications, with scripting languages and associated wrappers specially designed for this purpose.
Shell is a task-oriented user interface that utilizes a pre-established language to acquire and work with data via mathematical models. It is compatible with all leading operating systems, including Windows, Linux, and Mac OS X.
Shell commands and scripts are utilized for data collection, rendering it an enjoyable activity to work with data.
What is the Most Popular Programming Language for Machine Learning?
Let’s begin by looking at the popularity of programming languages for machine learning. While it is commonly utilized by machine learning engineers and data scientists, only 33% of developers consider Python as their first choice when developing new applications.
This is unsurprising, given the recent upswing in popularity of Python’s deep learning frameworks, including TensorFlow, as well as other libraries like PyTorch.
Python and R have similarities but differ significantly in terms of popularity. With 44% of users employing it, Python is the most widely used programming language and is preferred for machine learning projects by 17% of users. On the other hand, only 31% of users use R, with just 17% using it as their primary language for machine learning projects.
Thus, R is typically used as a supplementary language rather than a primary one. Python has the largest user base among all major programming languages.
The Future of Machine Learning Looks Bright.
It’s evident that the development of machine learning is a long-term trend. With the range of programming languages and frameworks at our disposal, there is potential to discover groundbreaking ways of using machine learning and artificial intelligence.
Before considering hiring a developer to help with machine learning, it’s crucial to have a well-defined concept of what you intend to accomplish. The technology team should establish a clear objective and strategy prior to initiating the search for a developer.
Python’s vast library and user-friendly attributes render it a top pick for various inexperienced developers. If your project demands their expertise, it can be easily sourced.
It’s a thrilling moment for machine learning, regardless of the programming language you use. If you’re interested in enhancing your development team with remote developers, feel free to contact us. We can assist you in finding dedicated, full-time programmers who are a perfect match for your organization’s culture, time zone, and business needs.