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Setting up the environment for Python

When you get sick of theory, it’s time to leave get some hands on experience with practical that excite.

Setting up the environment for Python/ We at Nucleus understand that ML journey without practicals is bread without butter. So here we’re with a comprehensive tutorial on writing your first ever ML code in Python. You heard it right, Python. To get started, you need some serious programming experience. Hey, just kidding. Serious programming experience is required at blogs made for programmers. Analytics Bay is good, it’s friendly, and it can empathize with you. Here all you need is excitement; loads of it. Let’s start by setting up the programming environment, the required software’s. There are two ways you can take off from here, one is with Google Collab, other with Anaconda.

Collab Simply hit this link up and you’re ready to code. This even works on your smartphone. https-//colab.research.google.com/ Anaconda Go to the link given below, download the version that best suits your PC requirements. Once the setup gets downloaded, install it. Once the installation is completed, open up Anaconda, then go to jupyter notebook. Done! You can probably start your ML journey from here and I am sure you’ll never need to worry about the environment anytime later. https-//www.anaconda.com/distribution/

The Libraries Python offers a plethora of libraries useful for machine learning, deep learning and maybe that is the sole reason why python is on fire when it comes to Data Science. Let us introduce you to some of the widely popular python libraries that’ll serve as your companions in your ML journey.

Numpy Pandas Scikit Theano Tensorflow Keras Pytorch Seaborn Hello World Let’s do it. Let’s do it for the first time (and probably the last) in your life. Let’s print the iconic hello world using python. To do this, open up anaconda. Next, look for a Jupyter notebook and open it up. Here we have, our notebook. Type in this and press enter to see the output.

“import hello world

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