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    Popular posts from this blog

    Unsupervised Learning

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    In unsupervised learning, we are provided with a dataset and nothing else. There is no outputs, parameters or anything to distinguish the given data. Our aim is to let the machine to learn or create a grouping/classification on its own. Unsupervised learning is comparatively difficult with respect to supervised learning. Consider social networking sites, based on our actions they are filling our feed with posts/media that we would prefer, suggests people we want to follow and a lot of things. These kinds of solutions are done using unsupervised learning. In the figure, social media users are grouped by machine based on some factors which are unknown to the programmer. This could be a simple example of unsupervised learning.
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    Automate Blog Post creation using Blogger APIs and Python

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    This article will discuss the following topics, Blogger's REST APIs. Google's OAuth 2.0 playground. Accessing spreadsheet in Python. Creating blogger posts via Python. An experiment to automate blog post creation when you have structured data. I was searching for IFSC codes for some bank transactions and came across Reserve Bank's website, which has a list of Excel files containing Bank Codes, Contact Info and related details of each bank's branch. This time I will be looking towards Bharat co-operative bank of Mumbai. Here is how the corresponding data looks like. In the above picture, each row represents lots of details about each branch of the Bank. Let's think of creating a list of blog posts where each post gives IFSC codes of a particular branch along with address and contact details. As the first step, I have created a blog in blogger. In the Blogger dashboard, please also note the parameter called blog ID which is a kind of unique identifier
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    Setting up Python Flask server on internet via Port forwarding

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    In this post we will cover topics; Starting a server using Python Flask. Accessing server in Local Network. Adding a Port Forwarding rule in NAT settings. Accessing server in Internet via public IP. Difference between static IP and dynamic IP. Step 1 :- As the first step, we need to setup a server on our system.A server exposes your code to a particular port.Some examples are Apache Tomcat, Spring boot (inbuilt), Python Django (inbuilt), Nginx. Here we have a simple server in Python Flask. For a GET request in root path the server returns a "Hello World!".That's it. Going to start the server on my local IP address(192.168.1.105) at default flask port(5000) using below commands, $export FLASK_APP=flaskTest.py $flask run  --host=192.168.1.105 Step 2 :- For security purpose all ports for external communication is closed by router's firewall by default. So no communication can be done to outside world. For same reason our server won't be
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    The beginning of Data Quest

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    Setting up Jupyter Lab integrated with Python, Julia, R on Windows Subsystem for Linux (Ubuntu)

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    Every guy gotta favourite OS. But, due to some constraints, all can't use that all the time. Being a Linux user, I had to shift to Windows 10 because of the barrier by MS Office and Dual booting often doesn't work. A few weeks before I learned about Windows Subsystem for Linux and my experience was great. If you are Linux pro stuck in Windows, this could be a relief. Basically, you can run a well-designed minimal version of Ubuntu inside Windows 10. In this article, we will get familiar with, Activation of Windows Subsystem for Linux. Installation of Ubuntu (no GUI) using Microsoft App Store. Configuring Python3, Pip3 and Jupyter Lab. Installation of R in Ubuntu and integration of R with Jupyter Lab. Installation of Julia in Ubuntu along with Jupyter Lab integration. Accessing Windows files with Ubuntu. Installation of Windows Subsystem for Linux Windows don't ship with WSL by default, we need to install it via PowerShell.  Reference -  https://docs.microsoft.co
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