IDEs are very important for running your code with a better experience. An Integrated Development Environment (IDE) generally consists of a code editor, a compiler or an interpreter, and a debugger which is accessible through a single graphical user interface (GUI). In this article, I’ll walk you through the three best IDE for machine learning that you can use for a better experience.
Best IDE For Machine Learning
Now if you are a newbie in programming I will never suggest you use an IDE because you have to be perfect at analyzing your mistakes on your own which you cannot be good at if you start your career in programming with an IDE. The best advice I can give you is to write some codes on a piece of paper. It helps in remembering and also helps in interviews because in an interview you have to write codes on a sheet and not on a computer.
Also, Read – Data Cleaning with Python.
Now coming back to An IDE, it is an environment used to write and create programs using tools such as an editor and a compiler. Here are the three best IDE for machine learning that I have experienced so far.
Pycharm
JetBrains developed PyCharm as a cross-platform IDE for Python. PyCharm is compatible with Windows, Linux and macOS. PyCharm is available in two different editions. The first community edition is accessible to all, free of charge and the second paid professional edition to pay.
The tools and functions provided by PyCharm allow programmers to quickly and efficiently write a variety of software applications in Python. It supports the following python versions 2.6 and 2.7 in python2 and 3.4 to 3.7 in Python3. Besides that, it supports JavaScript, HTML, CSS, YAML, etc., in frames, it supports Django, Google App Engine, Flask.
Atom
The developers of Atom call it as a “hackable text editor for the 21st century”. Atom allows users to install third-party software and themes to customize the functions and appearance of the editor so that you can configure it to your preference and with ease (the Atom). It is just as welcoming to a beginner as it is to an experienced developer.
Atom has more languages ​​available than you can use, having add-ons and support for my uses: CSS, PHP, HTML, JSON, JS and many other extensions with plugins is also your friend. Atom now also has built-in Rust support. Atom has a lot of custom themes, and this is the best feature I like about Atom to be one of the best IDE for machine learning.
Visual Studio Code
Visual Studio Code is a code editor developed by Microsoft for Windows, Linux and macOS. It generally includes support for debugging, built-in git control, syntax colouring, smart code completion, code snippets, and code refactoring.
A Python environment is a context in which a Python program runs. An environment generally consists of an interpreter or a compiler and any number of installed packages. The Python extension for VS Code provides useful integration features for working with different environments. The visual code is quite complete out of the box, but there are other plugins available in addition to them too.
Also, Read – Summarize Text with Machine Learning.
I hope you liked this article on the three best IDE for Machine Learning. Feel free to ask your valuable question in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.
