Since the world is rapidly changing and technology is evolving so quickly, machine learning has become an essential element of the technology stack. Machine learning can predict possible outcomes in large-scale complex systems. If you work with a lot of data, you will eventually need to select the best language for machine learning.
Different programming languages for machine learning are favored in various parts of the world. Before you learn the best programming language for machine learning and artificial intelligence, you should understand what machine learning is and in what areas it can be applied.
Machine Learning is a subfield of artificial intelligence that deals with constructing and studying algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. Machine learning algorithms depend on building mathematical models from data sets to analyze patterns and make predictions.
Machine learning is a branch of artificial intelligence focusing on pattern recognition, predictive analytics, data mining, and other forms of advanced statistical analysis. It allows computers to mimic human decision-making using mathematical models and sophisticated predictive statistics when applied to large amounts of data.
Machine Learning can be used in many areas and fields, creating a complete, up-to-date list is impossible. Every day, new technological solutions make it possible to apply machine learning where it seemed impossible yesterday.
The fact that machine learning can be used in the production of automated cars is no longer a surprise to anyone. But machine learning is widely used, for example, in medicine (for medical diagnosis and prognosis) or forensics (fraud detection). Machine learning has long been used for translations or facial recognition, e.g., via smartphones. Some people may be surprised that machine learning algorithms analyze financial media, trade, or product recommendations.
Currently, every technology corporation employs machine learning engineers who are working on how to implement machine learning models.
The most famous companies that use machine learning are:
To really understand machine learning, you need to have a foundation in programming languages and their essential functions, like algorithms, data structures, logic, memory management, etc. Machine learning does have libraries that can make it easier for programmers to put logical machine learning into practice. Still, nothing can replace reliable knowledge of popular programming languages.
There are many different programming languages in the world, so it's natural to wonder which one is best for machine learning. Here is a list of the best, in our opinion, machine learning programming languages.
Python is considered by many developers to be the best machine learning language.
Python is an uncomplicated, comprehensive programming language that can manage complex scripts and web applications. Although it was only established in 1991, Python has become increasingly popular due to its easy learning curve. In addition, the abundance of frameworks and libraries makes this language versatile for various uses.
Python's flexibility in terms of coding is well-known. Open source contains various visualization tools and fundamental building blocks such as Sklearn, Seaborn, and others, allowing for much customization. These robust libraries make programming simple, allowing computers to learn even more.
Python supports the following programming paradigms: object-oriented, functional, imperative, and procedural. It's great for prototyping, scientific computing, sentiment analysis, natural language processing, and data science. Python has two excellent machine learning libraries popular with Python developers: TensorFlow and Scikit.
Thanks to its many functions, R is a fantastic programming language that can be used by programmers and people who do not deal with programming (e.g., data explorers, data analysts, and statisticians).
R is a popular data visualization language that focuses on statistical computation. It is trendy among machine learning programming languages.
R is an excellent resource for developing machine learning applications because of its essential features. It has strong computing capabilities and can easily create graphs, which scientists find helpful when analyzing data. R is also used by huge businesses, particularly in biomedicine.
It's a dynamic and demanding functional language that is considered dynamic. R employs machine learning techniques such as classification, regression, decision tree construction, and others. It supports a wide range of operating systems, making it quite adaptable.
The Java Virtual Machine, which runs on any platform, is used to create programs that may be accessed from anywhere in the world. It's commonly used to develop applets for websites, large-scale business systems, and Android apps.
These languages are considered effective in machine learning projects. They are used to detect cyber attacks and fraud and improve data security on the Internet.
Julia is a popular programming language that is a huge competitor to Python and R language. It focuses primarily on analyzing models necessary to build machine learning applications. It has a simple syntax and is highly efficient. It works seamlessly across platforms and is functional and object-oriented. Due to the many solutions it offers, it is often the preferred option for developers.
Julia can be used on the server and client sides. When it comes to performing computational statistics and numerical computations, it's quite beneficial. As a result, statisticians in bioinformatics and analytics are attracted to it.
Lisp is one of the oldest programming languages that are still in use. It was created with a focus on artificial intelligence applications.
Because of this, the language is not very popular. In addition, using Lisp would require additional work to ensure compatibility with new hardware and software.
Scala is a programming language that mixes features of both functional and object-oriented languages. It ran on a Java Virtual Machine (JVM) and was created in 2001, partly to address deficiencies commonly cited about Java. In 2004, the first stable version was released-Scala 3 came out in May 2021 with many syntax simplifications compared to earlier versions.
Scala has been used to develop machine learning algorithms with great success. The primary advantage of the language is its speed and the ability to construct large-scale applications that utilize a lot of data, which helps make it ideal for scientific computing, linear algebra, and other random number generation purposes. Scala's libraries aid in the creation of mathematical computations, as well as different random number generating processes.
Despite its undoubted advantages, Scala is hardly the first choice language. Therefore, there aren't many developers that specialize in it. However, programmers familiar with Java consider Scala a straightforward language, so it is often easier to find a Java developer to learn than to find someone who already has the required program knowledge.
Many developers worldwide prefer C / C ++ for their versatility and popularity. These programming languages are low-level, so that computers can read them easily.
Torch, TensorFlow, and other libraries have been implemented using C / C ++ to great effect. They can provide optimized performance for critical applications. Additionally, C ++ offers more flexibility regarding algorithm changes and memory management than any other programming language. It gives developers greater control over various performance parameters.
C ++ supports object-oriented programming, inheritance, and feature reloading, which C does not. Many programmers consider it a niche language, but its popularity is still grand. The most popular uses for C++ are in applications managed by Windows, hardware drivers, and computer games. Any application written in C will also work under the new version because of compatibility between the two languages.
Go (Golang) is a popular machine learning programming language among data scientists because it can handle large data sets with multiple tasks performed together. The positive side of Go is that it can perform these tasks concurrently. Additionally, Go is a system-level programming language with a built-in vocabulary for common operations.
Due to its popularity among cloud computing services and features such as garbage collection and dynamic typing, it is considered one of the fastest-growing languages. It is similar to C, making it popular in serverless computing infrastructure.
Some people find Go easier to learn than other languages, and its security features make it appealing to developers.
The Unix shell, a command-line interpreter, served as the inspiration for the Shell programming language. Because its simple syntax makes Shell's scripting languages and wrappers an excellent choice for building machine learning tools, algorithms, and apps.
Shell is a powerful scripting language that helps you collect and prepare data using mathematical models. It's available for all major operating systems, including Windows, Linux, and macOS.
Scripts and shell commands aren't only for data collection and decision-making based on that data. It's an easy way to process information without complicated programming.
Above, we have presented a few programming languages that, in our opinion, are worth considering when planning to create machine learning apps. Of course, there will always be discussions about which language is best and whether knowledge of programming languages is needed to implement machine learning models. As always, it depends on the type of project and the problems to be solved. However, it is worth having at least a basic knowledge of programming languages and their essential functions to correctly choose the language for the project.
In addition to making it easier for developers to implement machine learning logic, machine learning has libraries that work with some standard programming languages.
However, if you need help choosing the correct programming language, please don't hesitate to contact us. We have built various applications for over 12 years and have extensive experience running such projects and consulting.
With 13 years of experience in the IT industry and in-depth technical training, Peter could not be anything but our CTO. He had contact with every possible architecture and helped create many solutions for large and small companies. His daily duties include managing clients' projects, consulting on technical issues, and managing a team of highly qualified developers.
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