Jupyter notebook : Take the first step towards machine learning and data analysis
Intent of this article is to exhibit how to setup and get started with Jupyter notebook (for beginners) which is a great tool for data analysis and machine learning programming.
Brief intro about Jupyter notebook:-
Jupyter notebook is a web-based interface for interactive programming where we can write code, execute it to see the output, create different visualisations as well as write beautiful documentation using markdown language. We can even download notebook and share it with others. It supports a variety of programming languages including Python and R. It won’t be fair to compare it with other IDEs (like VS code), rather it’s a complementary tool well equipped for data science related problems.
Installation and setup:-
For beginners, it is recommended to install Anaconda which conveniently installs Python, Jupyter Notebook and other commonly used packages for data science related problems. Follow this link to install it.
Once successfully installed, add below anaconda scripts path to your Path env variable (if not already added).
<base directory>\anaconda3\Scripts
To verify successful installation, open a command prompt and check jupyter version as below.
Run below command from any empty directory to open jupyter notebook and leave the command prompt open since it runs the jupyter notebook server.
jupyter notebook
This command will open jupyter notebook on the browser. This will be blank to start with. Any notebook we create here, will be saved in the empty directory where we ran the command from.
Click on New -> Python3 to create a fresh python notebook.
First of all, name your notebook properly by clicking on Untitled.
Now you can write any python code and run it. Below is a sample to print hello world message.
Below is a sample program using matplotlib to create visualisation.
Lastly, let me show how to write documentation in the notebook using markdown language.
Create a new cell and mark is as Markdown cell as below.
And write you documentation in markdown syntax. Sample below.
Congratulations on getting this far.
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