How do you build an intelligent system? The answer varies depending on the needs of your project and the resources at your disposal. But one thing remains certain no matter what: You need to code! But which programming language should you use to build intelligent systems? There are many factors to consider in making this decision, so read on to learn about the best languages for machine learning!
It’s no secret that Python and R are the two most popular languages used in machine learning. However, depending on the project you’re working on, they may not be your best choice.
This guide will help you discover the correct language to use in certain situations to complete your machine-learning project successfully and efficiently.
Whether you’re just starting out or already an expert, you’ll find valuable insights that can help improve your machine-learning workflow and make your projects more successful from this guide on the best languages for machine learning.
Choosing the right programming language
As a machine learning developer, you should already know that there are many different programming languages to choose from when you’re working on your machine learning project.
While many people favor Python as the best language for machine learning, there are other languages that can be useful.
When choosing the right programming language, you’ll want to consider what your main objectives are with respect to building a model and your personal preferences.
You will also need to determine if you have time constraints or need a specific software library that may only be available in one language or another.
Programming Knowledge Required for Machine Learning
How much programming knowledge is required for Machine learning mainly depends on how you want to use Machine learning. It totally depends on the type of usage and the machine learning models to tackle real-world problems.
If you want to unleash the real power of machine learning, it’s essential to have knowledge about algorithms, data structures, memory management, Object Oriented Programming concepts, logic building, and a few coding skills.
There are various programming languages for machine learning offering a rich set of libraries that make it easy for programmers to implement machine learning techniques more efficiently.
If you are not good at programming, there are various graphical machine learning tools like Orange, BigML, and Weka that provide an environment to implement all the machine learning algorithms without the need for coding.
Best Languages for Machine Learning
There is no specific language that we can say is the best language for machine learning. Of Course, every language finds its place where it fits best.
However, there are certain programming languages that are more appropriate for certain machine learning models or tasks than others.
Most Machine Learning projects require extensive knowledge of different languages, libraries, and frameworks which need to be chosen in order to allow the use of data representations such as matrices and arrays.
According to a survey, most machine learning scientists prefer python for sentiment analysis and Natural Language Processing (NLP) to languages such as R, Java, and JavaScript. While in contrast, for cyber-attacks and network security, they preferred Java.
Here we have profiled a list of the best programming languages for machine learning.
Python
Python is one of the most popular languages for machine learning due to its versatility and ease of use. It is an interpreted, object-oriented, open-source programming language. Python is used for web development and can also be utilized for machine learning purposes.
There is an increase in demand for python programmers as it is an excellent tool to process in-depth and detailed data with accuracy and speed.
Python provides a huge set of packages that helps to do machine learning at a fast pace. Moreover, open-source libraries available in python provide all the necessary tools needed by data scientists to carry out their work more efficiently.
According to recent stats, there are now more than 8.2 million developers across the globe who code in python. The rise of machine learning is a big factor in the popularity of the python programming language.
It is estimated that around 69 percent of machine learning developers and data scientists use python compared to 24 percent who use R programming language.
R Programming Language
R is another best language for machine learning with more than 2 million users. It is open-source and a powerful programming language used for data analysis, statistical modeling, and machine learning.
R supports Object Oriented Programming (OOP), imperative, procedural, and functional programming paradigms. It is a popular programming language among data scientists because it supports statistics and data visualization.
R provides a wide variety of statistical and graphical techniques and is highly extensible. Its code compiles and runs on various operating systems including Linux, Windows, and macOS.
R is an incredible programming language for machine learning. It contains a good resource pool and its great features help developers to develop highly effective and efficient machine-learning models.
With its user-friendly IDEs like RStudio, you can directly interact with R through its built-in packages and functions. R is a statistical programming language and is commonly used in data science to process large amounts of data, and perform statistical calculations.
According to R Foundation, R is an environment within which statistical techniques are implemented rather than a statistical system. There are about eight packages delivered with the R distribution and much more are available online.
Java/JavaScript
Java and JavaScript are two of the best languages used for machine learning. They are both open-source languages, which means that they are free to use and modify.
Java is the most commonly used programming language after python and R for machine learning. It is a versatile language that supports a wide range of algorithms and data structures. Java has a well-defined object-oriented model and a well-defined exception system.
Many big organizations use Java and JavaScript for their high-profile projects. Java has plenty of libraries for machine learning like JavaML is an in-built library for a collection of machine learning algorithms. Data scientists use it to detect cyber attacks, frauds, and network security.
The important feature of Java is scalability, which every machine learning engineer must consider before starting a project. The applications built using Java are highly scalable and that makes them a great choice for machine learning engineers.
C/C++
C and C++ programming languages enable developers to create efficient, reliable, and robust software systems. It has been used in a variety of industries, including computer systems, telecommunications, and scientific computing.
They are well-suited for machine learning, as it provides efficient numerical computation and robust data structures. The languages also have built-in support for distributed computing and various machine learning algorithms.
One of the main benefits of using C for machine learning is that it is a broadly accepted language. This means that developers can rely on existing C libraries and tools to build machine-learning applications.
C is also relatively easy to learn, which makes it a good choice for novice developers. Furthermore, C is a cross-platform language, which means that it can be used to create applications for a variety of platforms, including Windows.
There are plenty of libraries that are implemented using C/C++ just as Torch and TensorFlow. C++ is widely used for a variety of machine-learning tasks, including feature extraction, classification, regression, and prediction. It offers a variety of features that make it an ideal choice for machine learning engineers.
How do I start a machine learning project?
Machine learning uses programming to give machines the ability to automatically and independently make decisions.
If you’re just getting started with machine learning then it might be good to focus on a specific problem or type of machine learning like natural language processing or binary classification models.
There are many resources online for researching what kind of machine learning project would best suit your needs, so start there.
When selecting the best language for machine learning, consider how much time you want to spend managing lower-level details versus spending time thinking about your actual project.
Generally speaking, if you’re looking to build a project from scratch or explore new ideas then go with Python.
If you want more speed when building models but less flexibility when designing them then go with C++.
But if you want speed but don’t care about explicit memory management go with Java.
