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Machine Learning Projects With JavaScript [Top Libraries & Web Applications]

Aspiring web developers and programmers are always on the lookout for learning resources and hands-on activities to refine their skills. Various open-source projects demonstrate the required techniques from a detailed Javascript tutorial with examples to an innovative take on artificial intelligence. 

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Machine learning is one of the most popular emerging technologies in the digital era, led by big data. It involves feeding data into the software application and writing algorithms to build logic based on the data. Therefore, it is an artificial intelligence branch that predicts accurate outcomes with limited human intervention and explicit programming. 

Javascript is a go-to programming language for making interactive client-side applications. Developers also use Node.js to write server-side code in Java. Since machine learning is a growing field in tech, more and more practitioners are looking to gain knowledge and experience in this area. 

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The present-day web ecosystem has advanced to make room for varied machine learning problems. Neural networks can now run in any language, including Javascript. If you are thinking about implementing a java ML project, read on to know about some exciting libraries and web applications. 

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Machine Learning Projects in Javascript

1. TensorFlow

TensorFlow is an excellent AI library that contains Java APIs and lets you create data flow graphs and develop impressive projects. You can make use of Tensorflow’s flexible ecosystem tools and community resources to attempt machine learning with Javascript. You can use it to train networks in recognizing images, voices, text, numbers, etc. There is an input layer, a hidden layer, and an output layer in any such neural network. The network can also be used for diverse time series and regression problems. 

2. Brain

Brain.js is a reliable resource for creating neural networks and training them on input/output data. You can run the library using Node.js or load a CDN browser directly onto a web page. Brain.js performs GPU computations and falls back to Java when GPU is unavailable. Moreover, you do not need to have in-depth knowledge of neural networks to implement it. And you can easily integrate trained models on your website or import/export them to JSON format. You can read the full documentation and go through the live examples available on the website.  

3. Synaptic

It is a Node.js and browser library that allows developers to build any neural network they want. Synaptic is architecture-agnostic and boasts of an active maintenance community. You can test and compare different ML algorithms with its built-in architectures and go through a comprehensive introduction on neural networks. Synaptic contains many practical demos and tutorials that uncover machine learning and its working. 

4. NeuroJS

NeuroJs is an open-source framework that enables you to build AI systems with reinforcement learning. You can gain familiarity with the different parts of neural networks by studying one of its detailed demos of a 2D self-driving car experiment. The NeuroJS library uses pure JavaScript and several modern tools, including webpack and babel. 

5. ConvNetJS

ConvNetJS is a popular project on GitHub having features and tutorials, most of which are community-driven. As an advanced deep learning library for Java, ConvNetJS works entirely in your browser and supports many learning techniques. Initially, it was developed by a Ph.D. student at Stanford University and later extended by contributors. With ConvNetJS, you can expect to gain an understanding of the following things:

  • Common neural network modules
  • Specifying and training convolutional networks capable of processing images
  • An experimental reinforcement learning module
  • Classification and regression

6. FlappyLearning

This JavaScript project contains code for a machine learning library and implements the same in a fun demo of the Flappy Bird mobile game. FlappyLearning uses neuroevolution, an AI technique, and applies algorithms to play the game like an expert. The program can dynamically learn from every iteration’s success or failure, hence mimicking the process of a human nervous system. You can try the demo by running in your browser. All you have to do is open index.html and go! 

7. Land Lines

Land Lines is a web experiment that allows users to explore the Google Earth dataset without making any calls to the backend server. With machine learning capabilities, data optimization, and graphics card, the application can find satellite images similar to the users’ doodles. Land Lines can also work well on mobile devices. You can find the full source code of this project on GitHub

8. Thing Translator

Thing Translator is another instance of a web experiment that can serve as a javascript tutorial with examples. This application can make your phone recognize real-life objects and then name them in different languages. It has been built on web technologies using two machine learning APIs, namely, Cloud vision and Translate. Cloud Vision helps in image recognition, whereas the Translate API assists in natural language translations. 

9. Deep Playground

If you want to play with neural networks and dig into their components, you can check out the playground library on GitHub. It offers an educational web app complete with a UI (that lets you control the input data) and a number of neurons, algorithms, and metrics. The project documentation is open-source and written in the TypeScript language. 

10. DeepForge

DeepForge provides a developer-friendly environment for deep learning. It is based on Node.js and MongoDB, running directly in the browser. Here are some of its key features:

  • Aids design with a simple graphical interface 
  • Supports training models on remote machines
  • Possesses a built-in version control

11. WEKA

This free machine learning library for Java is inspired by the Weka bird, a flightless species found in New Zealand. It is a collection of algorithms focused on deep learning. You can learn the following skills with this project:

  • Data mining and data preparation tools
  • Classification, regression, and clustering
  • Visualization, and so on. 

12. Deeplearning4j

It is a deep learning library that makes use of distributed computing frameworks like Apache Spark and Hadoop. Deeplearning4j is compatible with virtual machine languages like Scala and Kotlin. It aims to bring AI to business environments with detailed API documentation and sample projects. 

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Wrapping up

Even though Python is usually the primary choice for machine learning projects, Java is equally capable of powering ML tasks. And there is a range of options available, as described above. So, get started on the path of improvement by completing a project and practicing some core machine learning techniques with javascript!

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

What are the cons of using WEKA?

Data pre-processing, classification, regression, clustering, association rules, and visualization are all tools included in WEKA. Despite the fact that WEKA may be connected with the Python programming language, customers find the procedure excessively time-consuming. WEKA does not offer a wide range of analytical choices; instead, it is limited to a few. When compared to other tools, WEKA does not support all IDEs. Therefore, connecting WEKA with a user interface designed by any other IDE requires extensive and sophisticated scripting.

What are the pros of using TensorFlow?

TensorFlow is built to work with a variety of client languages. Python, C++, JavaScript, Go, Java, and Swift are all officially supported. TensorBoard is a set of visualization tools included in the TensorFlow framework that make it simpler to comprehend, debug, and improve neural networks. It uses only a few lines of code to exhibit neural network graphs, input, output, training progress, and any other information in a clean, legible manner. TensorFlow makes it simple to share a trained model, which isn't a standard feature in other frameworks.

Which is better to use—Python or JavaScript?

Python has an advantage in terms of learning simplicity and widespread use in AI and ML. At the same time, JavaScript is more widely used because most developers are already familiar with it. At the end of the day, you can't go wrong with either language. As a result, there is no single or straight answer to the question. Since building a website using Python is a hard process, JavaScript is a better alternative if you want to use a programming language for desktop and mobile websites.

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