Best IDEs and open source platforms for Data Science and Machine Learning

Many of you already have basic knowledge about algorithms used in Data Science and Machine Learning as well as have mathematical skills but don't know where to write codes or from where to analyze a dataset and practice machine learning algorithms. There are a bunch of platforms where you can test your practical knowledge.

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There are two kinds of platforms to practice different kinds of models. 

1. CLOUD: Many people don't have high computational power systems on their own. They can't manage to invest a lot of money on systems just for high power, they can use online platforms. 

2. LOCAL: There are people who have high computational power systems and want to do all modeling on their own systems. They don't want to use an online platform for machine learning.

If you are looking to do some projects:

LOAN PREDICTION PROBLEM SOLUTION

WEB SCRAPING IN PYTHON

Let's talk about CLOUD platforms first.

• GOOGLE COLLABORATORY: Commonly known as 'Colab'. It is the best cloud-based notebook that allows you to model machine learning as well as deep learning projects without doing any setup on your own system. You will code everything directly on your browser. You get free access to GPUs without needing any high configuration hardware on your system. To try Google colab and learn more about it, you can visit the official page.

• AZURE NOTEBOOKS: Similar to colab, these notebooks also offer free hosted service to run and develop Jupyter notebooks without any installation on your system. You can use this notebook for very diverse scenarios. You can buy a corporate account or pay for other services as well. To try and learn more about it, visit the official page. 

• KAGGLE: It is the world's largest data science community. It is the hub of the data science and machine learning community. You can find and download plenty of notebooks to learn and engage and then use your knowledge to build your own notebooks to publish publicly. There are two types of notebooks on Kaggle. To try and learn more about it, visit the official page.

• AMAZON SAGEMAKER: It is a fully managed service provided by Amazon to build and deploy models quickly. To try and learn more about it, visit the official page.

All of the above cloud platforms are somewhat similar and somewhat different. You can try any of these and start learning through it if you don't want your own machine to perform huge computations.

Now let's talk about IDEs and open source distributions for local machines. There are numerous platforms that allow data science tasks.

• PYCHARM: It is an integrated development environment specifically for the Python language. You can access the command line and connect to a database. Its size is 250MB approximately. You can download it from here: Download Now!

• JUPYTER NOTEBOOK: Most of the cloud platforms use Jupyter notebooks to let user develop their model. It is one of the most used platforms among all data science practitioners. You can install the notebook directly into your system to use it on your own browser. You can install it in your system using these commands mentioned on their official page.

• SPYDER: It is an open-source cross-platform integrated development environment built for scientific programming in the Python language. You can download it from here: Download Now!.

• RSTUDIO: It is an open-source cross-platform integrated development environment built for the R language. You can also perform Python computations here but as the name suggests it is more suitable for R. You can download it from here: Download Now!

These are some of the local platforms which you can download on your system to use anywhere and anytime. But you need to have high configuration and compute power to run models on your system.

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