Hi!, Over the years I have developed multiple websites using multiple technologies such as HTML, CSS, JavaScript, and a couple of frameworks, but now I am expanding my skill set in the world of machine learning. I used JavaScript for everything and loved it; things were very easy and simple for me. I needed to create a static website, so I just used NodeJS with an EJS template to create a simple website. For something special and animations, I will use React. I needed to redo something on the server, so I got NodeJS. But now I am getting into machine learning, and everyone is using Python. So As a newsiest In machine Learning which is better Python or JavaScript.

Making Language more Powerful:

To be honest, when working with multiple languages, there is a chance that you will mix a few concepts, so you must concentrate on one thing, and that one thing must be good and best, So today we’ll look at which programming languages are good, easy to learn, and have an echo-system. All these things make a programming language more powerful; this is an echo system.

Python:

For Python, there are a couple of frameworks, and with these frameworks, we can extend the power of Python not just for desktop applications and machine learning but also for web development as well. The major frameworks are Flask and Django. Both of these frameworks are good, and using them we can deploy and display different machine learning models.

Examples of Python: Python is one of the official server-side languages used by Google; it is also used inside Instagram and Facebook infrastructures. Also, Quora, Netflix, and Spotify use it extensively, mainly to power their data analysis capabilities and backend services.

JavaScript:

JavaScript is loved and number one Programming language in the world, with that there is a way to learn machine learning using JavaScript Programming Language, There are a lot of framework for machine Learning, for example Node-JS, Using these modules you can work with JavaScript.

Examples of JavaScript Applications in Machine Learning

As mentioned above, JavaScript is primarily used for creating dynamic web pages. Therefore, its application in machine learning is mostly related to running ML models in web browsers (for example, to create an app that allows devices to recognize and identify objects appearing in the camera). One important feature of JavaScript that makes it suitable for some ML projects is the fact that it can run on most platforms and device types, lessening compatibility issues.

A popular open-source library that enables the deployment of machine learning programs in the browser is TensorFlow.js. It allows running existing models in the browser, training ready models with your own data, and developing new machine learning models directly in the browser. Another commonly used library is Brain, which enables the creation and training of neural networks and loading them onto a browser, e.g., to recognize color contrast.

Other prominent examples of using this object-oriented programming language in ML are as follows:

Deep playground

An educational web app that lets you play with neural networks and learn about their various components. It has a nice user interface that allows you to control the inputs, the number of neurons, which algorithm to use, and various other metrics that will be reflected in the end result. There’s also a lot to learn from the app behind the scenes - the code is open source and uses a custom machine learning library written in TypeScript and well-documented.

FlappyLearning

FlappyLearning is a JavaScript project that builds a machine learning library out of about 800 lines of raw code and implements it in a fun demo that learns how to play Flappy Bird like a virtuoso. The artificial intelligence technique used in this library is called Neuroevolution and applies algorithms inspired by the natural nervous system, dynamically extracting from the success or failure of each iteration. The demo is very easy to run - just open index.html in a browser.

Synaptic

Probably the most actively supported project on this list, Synaptic is an architecture agnostic Node.js and browser library, allowing developers to build whatever type of neural networks they want. It has several built-in architectures, allowing you to quickly test and compare different machine learning algorithms. It also has a well-written introduction to neural networks, a number of hands-on demos, and many other great tutorials that uncover myths about how machine learning works.

Land Lines

Land Lines is an interesting Chrome web experiment that finds satellite images of the Earth similar to those drawn by the user. The application does not talk to the server: it runs entirely in a browser and, thanks to the clever use of machine learning and WebGL, has excellent performance even on mobile devices. You can check out the source code on GitHub or read the full example here.

ConvNetJS

Although ConvNetJS is no longer actively supported, it is one of the most advanced deep learning libraries for JavaScript. Originally developed at Stanford University, ConvNetJS has become quite popular on GitHub, resulting in many features and community guides. It works directly in the browser, supports several training methods, and is quite low-level, which makes it suitable for people with a lot of experience in neural networks.

Thing Translator

Thing Translator is a web experiment that allows your phone to recognize real objects and give them names in different languages. The app is completely web-based and uses two machine learning APIs from Google - Cloud Vision for image recognition and Translate API for natural language translations.

Neurojs

A framework for building AI systems based on reinforcement learning. Unfortunately, the open-source project doesn’t have proper documentation, but one of the demos, the self-driving car experiment, has a great description of the various parts that make up the neural network. The library is written in pure JavaScript and built using modern tools such as webpack and babel.

Machine_learning

Another library that allows us to configure and train neural networks using only JavaScript. It is very easy to install both in Node.js and on the client-side, and it has a very clean API that will be convenient for developers of all skill levels. The library provides many examples that implement popular algorithms to help you understand the basic principles of machine learning.

DeepForge

DeepForge is a user-friendly development environment for working with deep learning. It allows you to build neural networks with a simple GUI, supports training models on remote machines, and has built-in version control. The project runs in a browser and is based on Node.js and MongoDB, making the installation process familiar to most web developers.

Is Python The Best For Machine Learning?

AI and machine learning development are fast-progressing, and there’s no shortage of their innovative applications in business. Some prominent examples include image classifiers, social media sentiment analytics, chatbots, predictive engines, or personalized recommendations (look here for more examples). To implement these tools and functionalities, data scientists, programmers and DevOps use a combination of programming languages, Python included.

So, is Python the best language for machine learning? For years, Python has been the language of choice for ML implementations. It provides a comprehensive library of packages with in-built functions that facilitate data analysis and processing, cleansing, modeling, visualization, and so on. These include TensorFlow, Keras, and Theano, all of which make it easy to implement various machine learning features. Many developers consider Python the preferred language for ML projects also because of its capability of interacting with other languages and platforms, and robust data handling capacity.

From a business standpoint, Python is used for machine learning projects for several reasons. First of all, it’s highly productive thanks to its design and has a ton of ready to use packages, which positively impacts the speed of implementation. Secondly, Python has a large community of developers and supporters. It’s estimated that there are more than 8 million Python coders around the world. This makes it easier to find people with the right skills to launch the project quickly. The extensive community support also enables daily code enhancements and regular creation of new libraries and packages that further accelerate the pace of development.

Final Thoughts:

For me, Python is the key, because of its industry and flexibility. Python is my preferred programming language because of its large package library.

Programming Language for Machine Learning: Python vs JavaScript