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tutorials

A project for developing tutorials for Streams

tutorials-1

Code for all my tutorials

tweet-generator

Train a neural network optimized for generating tweets based off of any number of Twitter users.

twitter-search-api-python

A Twitter search client mining tweets using their advanced search implemtation.

twitter-svm-tfidf.py

Twitter data mining with Python

ulmfit_text_classification

Transfer Learning for NLP Tasks

universities-clustering

For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public

url-shortener-ui

Training starter material for students. It contains the UI for the URL Shortener I created for the my college (DSCE) and now I'm using the same in the workshops I conduct in MIL @DSCE

useful-r-resources

This is a repo which will contain a list of some useful R resources.

voyager-example

vue-3-crud

Vue 3 example by a CRUD app with Axios, Vue Router, Bootstrap

vue-i18n

:globe_with_meridians: Internationalization plugin for Vue.js

vue-js-crud-example

Vue.js CRUD example App with Vue Router & Axios

vuejs3.0-jwt-authentication

VueJS JWT Authentication

vuetify-data-table-example

Vuetify data-table example with a CRUD App using v-data-table

vuetify-material-dashboard

Vuetify Material Dashboard - Open Source Material Design Admin by Creative Tim

web-scraping-in-python

Implementing Web Scraping in Python with BeautifulSoup There are mainly two ways to extract data from a website: Use the API of the website (if it exists). For example, Facebook has the Facebook Graph API which allows retrieval of data posted on Facebook. Access the HTML of the webpage and extract useful information/data from it. This technique is called web scraping or web harvesting or web data extraction. This article discusses the steps involved in web scraping using implementation of Web Scraping in Python with Beautiful Soup Steps involved in web scraping: Send a HTTP request to the URL of the webpage you want to access. The server responds to the request by returning the HTML content of the webpage. For this task, we will use a third-party HTTP library for python requests. Once we have accessed the HTML content, we are left with the task of parsing the data. Since most of the HTML data is nested, we cannot extract data simply through string processing. One needs a parser which can create a nested/tree structure of the HTML data. There are many HTML parser libraries available but the most advanced one is html5lib. Now, all we need to do is navigating and searching the parse tree that we created, i.e. tree traversal. For this task, we will be using another third-party python library, Beautiful Soup. It is a Python library for pulling data out of HTML and XML files.

website

Coding Train website

why-is-my-validation-loss-lower-than-my-training-loss-

In this project i will tell you hee three primary reasons your validation loss may be lower than your training loss when training your own custom deep neural networks.

yelp-reviews-into-1-star-or-5-star

In this NLP project you will be attempting to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews.

youtube-downloader-in-python

Download youtube videos and select the quality and the destination where you want to save them.