This post discusses some of the most popular programming languages for machine learning. Which one is right for you? This post discusses some of the most popular programming languages used in machine learning. Which one is right for you?
There are many data experts who devote their careers to mastering machine learning, a subset of artificial intelligence. If you’re new to data analytics or data science and are interested in machine learning, you’ll need to learn a few skills. These include some basic knowledge of programming as well as theoretical knowledge.
The question is, which programming language is the best for machine learning? We’ll explore what machine learning is before looking at some languages you might want to consider adding to your toolbox.
What is Machine Learning?
Computer algorithms that learn without being explicitly programmed by humans are known as machine learning (ML) which is a subset of Artificial intelligence (AI). In spite of the fact that they start with basic instructions from human designers, machine learning algorithms learn and make predictions on their own. In this way, they ingest training data and identify patterns and trends. As we will see, this information can be applied in many different ways.
The Machine Learning applications
We use machine learning when creating unique algorithms that are beyond the power of humans. There are times when the volume of data is so large that you must be reborn several times to handle it manually! It is no longer possible to survive without big data, so machine learning has become an essential part of our lives.
Machine learning algorithms can be used in computer vision, attempting to comprehend digital images or videos based on the information they receive. The use of computer vision in medicine allows it to diagnose diseases based on patient scans. In self-driving cars and military drones, navigation systems can also be programmed by analyzing visual data.
Let’s consider Natural Language Processing. NLP involves algorithms that learn the nuances of human languages. Language translations, internet searches, and even spam email detection are all applications of NLP.
Machine learning has thousands of applications, ranging from detecting fraud to solving complex math problems. Basically, machine learning is a major part of our world, and this share is growing. So why not start a career in machine learning now if you’re thinking about it?
What skills to learn for Machine learning?
You need some basic data analysis skills to enter the world of machine learning. This requires fluency in at least one programming language. Machine learning involves especially manipulating data. It is important to be able to build algorithms and understand the inner workings of machine learning concepts. Machine learning engineers spend more time writing code than developing statistical models.
Learning a programming language is a secondary priority, but mastering the basic concepts of machine learning should be your top priority. You cannot choose suitable models and solve machine learning problems if you lack basic knowledge of statistics, deep learning, systems processing, design, etc. Therefore, make understanding machine learning theories a priority on your list of skills. Next, you should improve your programming skills. What are the best programming languages for machine learning?
It is a good idea to learn a language like Python if you are new to machine learning and data analysis since Python syntax is easy to learn. However, if you are already an experienced programmer and work with C++, you should stick to what you know. As far as machine learning is concerned, there is no such thing as the best programming language. There are some languages that are more commonly used than others.
The best programming languages for machine learning
There are several programming languages that are commonly used for machine learning, and the best one for a particular project will depend on the specific requirements and the type of problem you are trying to solve. In 2019, GitHub introduced the best programming languages for machine learning in a survey. Some of the most popular programming languages for machine learning have selected and presented below:
Python
This language has grown in popularity in recent years and has overtaken C++ in areas such as data analysis and machine learning. Python is a high-level, general-purpose programming language that is easy to learn. Python is an excellent tool for learning and rapid prototyping because of its simple syntax and short learning curve.
Despite its slowness as a high-level programming language, Python provides a huge library that caters to almost everything (from gaming and special effects to data analysis and artificial intelligence and machine learning). Python is a versatile and agile programming language that is worth learning even if you’ve used more complex programming languages in the past.
Python is similar to Java in terms of machine learning, but is primarily used for scientific and non-commercial purposes, such as sentiment analysis and natural language processing. Python’s rise in popularity is largely due to the fact that Python has grown along with the field of data science. As a result, these two now have almost a symbiotic relationship.
A prominent feature of Python is having a repository called the Python Package Index , which contains thousands of libraries. A number of these libraries are especially designed for machine learning. Let’s discuss some of python machine learning libraries. TensorFlow, for example , allows beginners as well as experts to train machine learning algorithms with minimal effort. NLTK is an excellent tool for working with linguistic data, while Keras is a popular neural network library. Python is not the fastest programming language, but it is a gold standard for people interested in scientific computing and machine learning.
R language
The R language is a functional programming language that is used primarily for data analysis. In the early 90s, Auckland University in New Zealand developed the R language, which is derived from an older language called S. It is popular among scientists, statisticians, and other academics. Since then, the language has grown a lot and now supports object-oriented programming (which is essential for machine learning).
In fact, the popularity of R among statisticians explains why machine learning professionals are interested in it. Users are able to use specialized statistical techniques using a large number of user-developed extension packages, one of R’s main strengths. Currently, the Comprehensive R Archive Network (CRAN) contains over 15,000 packages.
It is common for R to be used alongside other languages in machine learning. However, it can also be used by itself for sentiment analysis. In addition to bioengineering (the study of large amounts of biological data), bioinformatics (the analysis of large amounts of biological data), and ecology, R is also suitable for statistically intensive machine learning processes.
Many R packages are designed to simplify those machine learning processes that require processing large amounts of data. For example, the Classification and Regression Training package makes building predictive models much easier. Randomforest can build random forest algorithms using decision trees, and packages like ggplot2 and plotly are great for data visualization.
C++
C++ is an object-oriented and general-purpose programming language. This language was released in the 1980s as a type of system language and for building system architecture. Although C++ is a difficult language to learn, it has proven to be highly efficient in tasks where performance is critical. Currently, this programming language is used to create desktop applications, video games, and even to program spaceships.
C++ is a low-level language that communicates with a language close to machine language level. For this reason, learning and mastering it may take some time. However, C++ has ideal performance due to the high speed of execution in processes such as memory manipulation.
In machine learning, C++ users can individually manipulate algorithms and manage memory resources. This is why this language is so efficient in artificial intelligence; Where speed is essential in analyzing large data sets. Although C++ is not suitable for rapid prototyping, it is still one of the most popular languages among data analysts and machine learning engineers.
C++ is very popular in fields such as robotics and game development that require high responsiveness. Let’s also say that machine learning is growing rapidly in these fields. In addition, C++ has many advanced libraries in the fields of machine learning and artificial intelligence. For example , Caffe is a framework for deep learning, DyNet is a neural network library, and Shogun is an open source machine learning library that includes various machine learning models.
Java
Like C++, Java is an object-oriented programming language. Java syntax is almost as complex as C++. However, it is not that low level compared to C++. Java is also a general purpose programming language. Java creates applications that can be run on all platforms through the Java Virtual Machine, which is a type of system emulator. This programming language is generally used in making Applets for web pages, large-scale enterprise systems and applications that are installed on Android mobile phones.
Java has a long history in professional and business fields. Traditionally, Java users have been employed in financial institutions and industry and business organizations. Now this language is also used in areas such as networking, cyber security and fraud detection. Many people use Java in machine learning because they are used to using it in enterprise development projects.
Java is a very scalable language, which makes it ideal for building complex and large-scale machine learning algorithms. Many big data frameworks such as Hadoop, Hive and Spark used in machine learning are based on Java. The Java Virtual Machine allows users to rapidly build and deploy machine learning tools. For all the above reasons, tech giants like Twitter, LinkedIn, and Facebook use Java to manage their big data.
Java also has many machine learning libraries and tools. For example, Weka is a Java desktop that is used for data mining, analysis, building predictive models, and visualization. The Massive Online Analysis framework is used for data stream mining and includes machine learning algorithms for processes such as classification, regression, clustering, etc.
JavaScript
If you have been asked if Java and JavaScript are related, we have to say no. These two languages are designed based on completely different rules and provided by two different organizations.
Unlike the languages we’ve talked about so far, JavaScript is a high-level programming language; In the sense that it has a simpler syntax and is easier to learn. JavaScript was originally designed as a scripting language, but later became a general-purpose programming language, and now you see it in browsers and in forms such as pop-up messages or clocks that display the time live.
Although JavaScript is a great choice for front-end programming, it is also used in the back-end of websites and applications. JavaScript is often used as an API for applications written in languages that do not have the high-level functionality of JavaScript.
Although JavaScript does not have the speed of lower-level languages, it has useful web applications for use in machine learning. For example, developers often pipe the output of machine learning algorithms into web-based dashboards that are Java-based. However, JavaScript is not a suitable option for use in heavy processes and cases that require a lot of work. While machine learning often deals with complex numbers, JavaScript involves relatively basic mathematical operations.
The number of JavaScript libraries for machine learning is increasing. For example, math.js has increased the flexibility of JavaScript and has given this language great computing power. In addition, it supports packages developed for other languages, such as TensorFlow.js (originally developed for Python). This allows JavaScript developers to run existing machine learning models, retrain them, and build new ones. JavaScript also has libraries such as Synaptic , which is a neural network library capable of mimicking brain function, and image processing tools such as OpenCV.js .
