Machine Learning has surely added a lot in terms of technological scope for innovation. With a lot of enthusiasts in, it surely will give the best returns in it. In our previous articles, we had discussed which would be the right one to code in Machine Learning.
In it, we had concluded that one may choose as per its own likings and work requirements.
So here taking it to be a base of the previous article we will be reviewing some well known IDE/Editors in python. Why python? Because it is one of the most widely used languages for machine learning and there is a high probability that a beginner would choose it over R because it is relatively easier to understand.
Let us start with the basics to understand what basically is an editor or an IDE. So IDE stands for Integrated Development Environment. An IDE is nothing but the software in your system which will help you with many comprehensive capabilities. The basic segments in an IDE are: source code editor, build automation tools and a debugger.
With all the support from different packages, be it inbuilt or not, an IDE will help you to set your code up with a console where one can see the output.
Different IDE’s for Python
Let us take a look at the most popular Python IDEs used for machine learning implementations:
And many more.
To keep articles brisk and fathomable I will discuss a few IDE’s and will answer the following questions.
- Which IDE for what purpose?
- How to download and get started with them?
- Having a wide knowledge of different IDE good or not?
Firstly we need to understand that it is always advisable to use IDE’s as per your requirement and work. You should work with the one which your system supports the best. A lighter IDE with not many facilities is better than the one which is dense and takes a considerable time to open and operate on your system. So here one needs to have the wisdom to select the things on the basis of the system requirements. And one should definitely be reading the system requirements before downloading any IDE for the same purpose.
Now coming up with the IDE’s I prefer. Yes, you read it right: “IDEs”. I myself don’t stick to a single IDE. It is really important for one to understand things don’t really go that smoothly if we stick to only one. When it comes to implementation, output and completion of work are more important than sticking to an IDE. So it is really important to have more than one in your system because it acts as a backup like if in case it didn’t work in IDE-A, I have backed the plan up with IDE-B.
Which IDE/platforms do I use and why do I do it?
When I was a beginner it was important for me to understand the concept of indentation in python so I had started off with IDLE. It is a great platform to learn the basics of code, learn more about bugs and indentation. The best thing about IDLE is that it is inbuilt and comes with Python. So when you install python an IDE, IDLE gets installed automatically into your system. You can run your code by pressing F5.
So if you are a beginner and want to learn things the easy way, you can go for IDLE.
Also, you can install python from here.
Well, there are a lot of themes for these IDE’s and I have changed them as per my convenience. When you will install them they will come with a default orientation of light background.
Pycharm is basically a professional IDE for developers which comes in two versions: one is a community version which is open-sourced and free to use. The other one is the professional version which can be downloaded for a 30-day free trial. You can install the Pycharm community version for windows from here.
Pycharm sets good for its distinct distribution of project folders which help us in saving all the related files in the same folder locally. Also supports many interpreters for the same purpose. One can also install a lot of different and unique packages from its project interpreter which can completely revolutionize the way we install packages. I use Pycharm for computer vision and normal python based projects. And I find it really useful in the same because of the features that I have mentioned above. You can run your code by right-clicking and then going to run.
3. Spyder IDE
Just like every other IDE, it comes with a light background theme, I have switched it to a darker because that is what developers do. For you to install Spyder IDE, you ought to have Anaconda Environment in your system.
To install Anaconda Environment, click here.
Anaconda basically is a data science toolkit which supports numerous platforms in it.
A glance to its platform is:
To start with a spyder you can either go to your search bar and type spyder or can launch it from the anaconda navigator as shown in the picture above.
I use spyder for machine learning, computer vision and deep learning purposes. One benefit of using spyder is you are able to debug the code line by line all by yourself. To run a code segment in here you need to select that specific segment and press ctrl+enter.
There are many other platforms that I use like Jupyter notebook, Google Colab and VS code but certain things don’t fall into the same category. It was really important for me to tell you the easy and the most popular ones that are used for the same.
To get a tour of the IDE you can surely search related videos on Youtube which would surely help you out in giving a tour cum tutorial on the same.
The article was aimed to discuss the list of different IDEs we have for python when it comes to machine learning and understanding that a certain IDE suits the best for a certain purpose. It needs to be identified on your own. There are no impositions on choices; one can use the same IDE for all his work but it is always advised to use different for different purposes.
In this article, we also saw that having hands-on different IDE is always better than sticking to only one because it gives you choices and you back up the work becomes easier in case the compiler of a certain IDE crashes, you know that you have another to back you up.
Hope you liked the article.
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Understanding and building fathomable approaches to problem statements is what I like the most. I love talking about conversations whose main plot is machine learning, computer vision, deep learning, data analysis and visualization. Apart from them, my interest also lies in listening to business podcasts, use cases and reading self help books.