codedamn

How to do machine learning in JavaScript?

How to do machine learning in JavaScript?

Machine learning is a branch of computer science that enables software to make data-driven predictions. In other words, it trains programs to understand data and make logical conclusions based on that information. The benefits of machine learning are numerous and diverse. It can be used in applications such as prediction and recommendation systems, document recognition, speech recognition, spam detection, sentiment analysis, and much more. In this article, we’ll discuss what is machine learning, why you would want to learn ML in JS if you’re a developer, and how you can use JS to implement machine learning in your own projects.

Live New Year Sale. Become The Best JavaScript Developer 🚀
Get access to hunderes of practice JavaScript courses, labs, and become employable full-stack JavaScript web developer.
  • Free money-back guarantee
  • Unlimited access to all platform courses
  • 100's of practice projects included
  • GPT-3 Based Instant AI Help
  • Structured Full-Stack Web Developer Roadmap To Get A Job
  • Exclusive community for events, workshops
Go Pro Now (50% Discount For Limited Time)

What is machine learning?

Machine learning is a branch of computer science that enables software to make data-driven predictions. In other words, it trains programs to understand data and make logical conclusions based on that information. There are many different types of machine learning. A few of the most common are classification, regression, clustering, and anomaly detection. In classification, data is organized into categories. Regression involves predicting future outcomes based on historical data. Clustering groups similar pieces of data together, and anomaly detection alerts users when it detects unusual data. Machine learning is the process of training a computer to learn on its own without being explicitly programmed. In other words, machine learning enables computers to process data and make logical conclusions without being explicitly programmed.

Now that we understand what machine learning is, let us try to understand why we would want to use JavaScript for machine learning.

Why do machine learning in JavaScript?

Python and R seem to be the popular choice as well as the industry standard when it comes to machine learning applications. But there are several reasons why you might want to use JavaScript for certain machine learning applications. Let us look at a few advantages of using JavaScript for machine learning,

Easily integrate machine learning with web and mobile apps

The simple integration of JavaScript machine learning with web and mobile applications is a huge advantage. Operating system support for Python on mobile devices is still in its infancy which forces us to use client-server architecture and deploy a python model on a server to use as API. However, there is already a robust selection of cross-platform JavaScript development tools for mobile apps, such as React Native, Cordova, and Ionic. Google has even released a specialized version of TensorFlow.js for React Native.

Machine learning in JavaScript can be easier to learn than in Python

Don’t get me wrong, Python is an amazing language and it’s great for beginners to learn to program. But Python is also an “expressive” language that can make things complicated very quickly. In Python, a single line can have a huge amount of functionality; this is fantastic when you know the language and the subject, but not so great when you’re trying to learn something completely new.

Privacy

Client-server topologies are used by the majority of machine learning applications. Wherever the machine learning models are operating, users must transmit their data there. The client-server design clearly has advantages. Through web APIs, developers may run their models on servers and make them accessible to the user apps. Developers are now able to use extremely big neural networks that cannot be run on user devices.

However, performing the machine learning inference on the user’s device is often preferred. Users might not want to submit their emails, private chat messages, or images to the server hosting the machine learning model due to privacy concerns, for instance. This is where JavaScript shines as we can directly run the model on the user’s client without sending their data outside of the user’s device.

How to do machine learning in JavaScript?

Now that we understand what is machine learning and why we would want to use JavaScript for machine learning, let us look at how we can do machine learning with JavaScript. Just like Python has great machine learning libraries like scikit-learn and PyTorch, JavaScript also has some neat libraries to get you up and running with machine learning. Let us look at a few of them below,

Brain.js

Brain.js is a library for performing and using neural networks using JavaScript and Node.js. It aims to hide the complexity of maths and offers a variety of neural network implementations that can be used and trained to do various tasks as per your needs. Brain.js is also very fast and tries to perform complex computations on the GPU when available.

If you want to get started with Brain.js, you can start with the getting started guide or view live examples to get an idea of the library.

TensorFlow.js

TensorFlow.js is a hardware-accelerated and open-source JavaScript library that can be used to develop machine learning models in JavaScript and use machine learning directly in the browser. It works in both JavaScript and Node.js and it can also be used to run or retrain existing models.

If you want to learn TensorFlow.js, the official tutorials are the best places to get started. You can also view demos and run them on your browser.

ml5.js

ml5.js is an open-source library that is built on top of TensorFlow.js in order to make machine learning understandable for a large audience of designers, programmers, and scholars. Using TensorFlow.js as its foundation and with no other external dependencies, the library offers browser-based access to machine learning techniques and models.

The official getting started guide is very thorough and is a great starting point for learning the library.

Conclusion

Machine learning is a very powerful tool that can be applied to solve a wide variety of problems. Because it’s a skill that is in high demand, now is a great time to get started with machine learning. There are many use cases for machine learning; however, the most common uses are image recognition, predicting customer behavior, and sentiment analysis. If you’re a developer and you’re looking to get into machine learning, JavaScript is the perfect fine language to use. If you’re interested in machine learning, this is the perfect time to get started. And if you are getting started with machine learning with JavaScript, I hope this article has helped you understand what it is, how it works, and why it’s a great beginner language for machine learning.

If you have any questions regarding this article or want to talk about machine learning, you can find me on Twitter. Thank you for reading!

Live New Year Sale. Become The Best JavaScript Developer 🚀
Get access to hunderes of practice JavaScript courses, labs, and become employable full-stack JavaScript web developer.
  • Free money-back guarantee
  • Unlimited access to all platform courses
  • 100's of practice projects included
  • GPT-3 Based Instant AI Help
  • Structured Full-Stack Web Developer Roadmap To Get A Job
  • Exclusive community for events, workshops
Go Pro Now (50% Discount For Limited Time)

Learn programming on codedamn

Codedamn is an interactive coding platform with tons of sweet programming courses that can help you land your first coding job. Here's how:

Programming is one of the most in-demand jobs today. Learning to program can change your future. All the best!

Sharing is caring

Did you like what Supantha Paul wrote? Thank them for their work by sharing it on social media.