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Popular Programming Languages for Machine Learning


Every day, whether you realize it or not, you come into contact with machine learning. Machine learning is used every time you fill out a captcha, use Siri, chat with an online customer service bot, or scroll through Netflix recommendations.

If you are looking forward to being someone behind all these functions, then we recommend you to enroll in Machine Learning Course Online

Alt-Text: Popular Programming Languages for Machine Learning 

The following questions keep appearing on Discussion forum sites and data science training boards: What language should I learn if I'm new to data science? What is the most effective machine learning language?

 This blog will be covering:

  • What is Machine Learning?
  • Programming languages used for machine learning?
  • Which language can be regarded as “The language” for Machine Learning?
  • Conclusion

What is Machine Learning? 

Machine learning is the process of teaching a computer to make predictions on its own. A Machine Learning Engineer, for example, might develop an algorithm that a computer can use to recognize patterns in data and then determine what the next part of the pattern should be.

Patterns can be found in a wide range of contexts and serve a wide range of functions. Typical machine learning examples include:

  • Speech recognition: Any speech recognition application makes use of machine learning to recognize what you're saying and convert it into text that the computer can understand.
  • Social media platforms, which generate ads and suggestions based on your likes and interests, are the most well-known machine learning application.

 Programming languages used for machine learning? 

While there are a variety of options to choose from, here are some of the really popular ones mentioned for you:

  1. Python

Python is a popular programming language because of its straightforward syntax and readability. Machine learning algorithms can be complex, but engineers can create the best solution for the problem at hand by using flexible and easy-to-read code. 

Python supports a diverse set of frameworks and libraries, providing engineers with greater flexibility and opening up new possibilities. 

Machine Learning Specialists can solve their problems in the most direct and efficient way possible by utilizing Python's many libraries. These libraries cover a broad range of topics, including artificial intelligence, natural language processing, and deep learning. Some of the most popular Python libraries for machine learning are as follows:

  • Numpy
  • OpenCV
  • Matplotlib
  1. C++

C++ is another widely used programming language for performance-critical applications that require memory management and speed. Because of these characteristics, it is an excellent programming language for machine learning. 

C++ is a powerful programming language that can manipulate algorithms and manage memory in great detail. It can also be used to create well-coded and fast algorithms due to its speed and efficiency. 

Among the machine learning and artificial intelligence libraries included in this top pick are:

  • DyNet
  • LightGBM 
  1. R

R is primarily concerned with numbers and incorporates a wide range of data sampling, model evaluation, and data visualization techniques. It's an extremely powerful language, especially when dealing with massive amounts of statistical data.

A Machine Learning Engineer can understand statistical data using R and then apply those principles to large amounts of data at once. Its solutions can assist an engineer in simplifying data so that it does not become overwhelming.

R comes with its own set of packages that engineers can use to complete tasks quickly, including:

  • PARTY
  • rpart
  • Shiny
  • Rmarkdown

  1. JavaScript 

Java and JavaScript are two of the most prominent and versatile programming languages available in the IT sector today. Because these are the languages used to create the vast majority of websites, incorporating them into machine learning simplifies the integration process. 

Both Java and JavaScript are well-known for their dependability and capacity to handle large amounts of data. Every language has its own set of machine learning libraries.

Machine learning libraries in JavaScript 

Synaptic

TensorFlow.js

OpnCV.js

Math.js

 Java machine learning libraries: 

  • Neuroph
  • Weka
  • Arbiter
  • JavaML 

Which language can be regarded as “The language” for Machine Learning?

 “Best machine learning language" is a myth in itself, there is no language that can be regarded as the best, it totally depends on the person.

According to our findings, popularity is not a good criterion to use when choosing a programming language for machine learning and data science. There is no such thing as the "best machine learning language," and what you want to build, where you're coming from, and why you got into machine learning in the first place all influence your decision. Most developers port their existing language into machine learning, particularly if they plan to use it in projects related to their previous work, such as engineering projects for C/C++ developers or web visualizations for JavaScript developers. 

Conclusion

If machine learning is your first exploration into programming, people often suggest enrolling in a Machine Learning Course. However, if you want to work in an enterprise environment with a broader perspective, considering a relevant Data Science Course alongside can be immensely beneficial. Whether you're diving into machine learning or data science, and regardless of the language used, the journey will be mind-boggling and rewarding. Have a safe and enjoyable ride on this exciting learning adventure!

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