Pinned Repositories
Apriori-Algortithm-Flask
deduplication-slides
"1 + 1 = 1 or Record Deduplication with Python" Jupyter Notebook
europython2018_boring
Code and Samples for the Europython 2018 Talk "The Boring Python Office Talk"
How-to-Create-Chat-Application-Using-Codeignter-PHP-MySql-Ajax-Jquery-Json
python-deepdive
Python Deep Dive Course - Accompanying Materials
msuryaprakash's Repositories
msuryaprakash/face_recognition
The world's simplest facial recognition api for Python and the command line
msuryaprakash/desktop_cleaner
msuryaprakash/pbpython
Code, Notebooks and Examples from Practical Business Python
msuryaprakash/sigma_coding_youtube
This is a collection of all the code that can be found on my YouTube channel Sigma Coding.
msuryaprakash/AutoTimer
msuryaprakash/Flutter-Zone-Food-App
Food delivery app written in flutter
msuryaprakash/pyqt5-qtquick2-example
An example of QtQuick 2 providing material and fluent design themes in PyQt5.
msuryaprakash/InstaAutomator
msuryaprakash/python-youtube-tutorials
msuryaprakash/cf-restaurant-online
CF Restaurant Online is a Community-Maintained Version of Online Restaurant Management System by Coders Folder
msuryaprakash/code_snippets
msuryaprakash/ProjectInitializationAutomation
msuryaprakash/publishing_python_packages_talk
Resources and slides from the talk "Publishing (Perfect) Python Packages on PyPI"
msuryaprakash/python-app-with-electron-gui
A better way to make GUIs for your python apps
msuryaprakash/oracle-db-examples
Examples of applications and tool usage for Oracle Database
msuryaprakash/pycon-2019-tutorial
Data Science Best Practices with pandas
msuryaprakash/WeatherPredictor
msuryaprakash/django_crud
Django CRUD Example Apps
msuryaprakash/python-cx_Oracle
Python interface to Oracle Database conforming to the Python DB API 2.0 specification.
msuryaprakash/30-Days-of-Python
For the next 30 days, learn the Python Programming language.
msuryaprakash/PyBot-A-ChatBot-For-Answering-Python-Queries-Using-NLP
Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.
msuryaprakash/youtube_tutorials
Collection of scripts corresponding to LucidProgramming YouTube tutorials
msuryaprakash/CIMembership
cimembership.io
msuryaprakash/electron-quick-start
Clone to try a simple Electron app
msuryaprakash/pyqt5
PyQt5 from riverbank
msuryaprakash/Build-an-AI-Startup-with-PyTorch
test
msuryaprakash/PySimpleGUI
Launched in 2018 Actively developed and supported. Supports tkinter, Qt, WxPython, Remi (in browser). Create custom layout GUI's simply. Python 2.7 & 3 Support. 100+ Demo programs & Cookbook for rapid start. Extensive documentation. Examples using Machine Learning(GUI, OpenCV Integration, Chatterbot), Floating Desktop Widgets, Matplotlib + Pyplot integration, add GUI to command line scripts, PDF & Image Viewer. For both beginning and advanced programmers .
msuryaprakash/pythonBilling
msuryaprakash/IoTMaster
msuryaprakash/Electron_ShinyApp_Deployment