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node-js-jwt-auth

Node.js Demo for Token Based Authentication (JWT) with MySQL database

node-js-jwt-authentication-postgresql

Node.js JWT Authentication & Authorization with PostgreSQL example

object-avoidance-in-drones-by-yolov3

opencvjs

JavaScript Bindings for OpenCV

openflutterecommerceapp

Open Flutter E-commerce App

otptextfield

A flutter package for the OTP Field widget.

path-planning

pitq

postman-collection

A Postman collection for SparkPost

programming-competitions

programming-for-everybody-getting-started-with-python-

my codes and certification in the coursera course "Programming for Everybody (Getting Started with Python)"

python

All Algorithms implemented in Python

python-classes-and-inheritance

About this Course This course introduces classes, instances, and inheritance. You will learn how to use classes to represent data in concise and natural ways. You'll also learn how to override built-in methods and how to create "inherited" classes that reuse functionality. You'll also learn about how to design classes. Finally, you will be introduced to the good programming habit of writing automated tests for their own code. The course is best-suited for you if you are already familiar with Python fundamentals, which are covered in the "Python Basics" and "Python Functions, Files, and Dictionaries" courses (courses 1 and 2 of the Python 3 Programming Specialization). It is optional to have taken the "Data Collection and Processing with Python" course (course 3 of the specialization), but knowledge of retrieving and processing complex nested data is helpful. This is the fourth of five courses in the Python 3 Programming Specialization.

python-flask-mongoengine-docker-starter-master

pythonrobotics

Python sample codes for robotics algorithms.

pytorch-3d-point-cloud-generation

Pytorch code to construct a 3D point cloud model from single RGB image.

reinforcement-learning-coursera-solutions

This is repository to maintain all solutions of Reinforcement learning course on coursera by University of Alberta and Alberta Machine Learning Institute.

requirements

🚦 Check and align required software versions in your project

sampiler

semester-4

DS

sequences-time-series-prediction-in-tensorflow

This Repo includes my work in the Sequences and Time Series course of the Tensorflow in Practice Specialization by deeplearning.ai at Coursera

sic-xe

sicas

An SIC/XE assembler written in python

sicxe

sign-language-mnist

sis

Simple image search engine

smart-bin

smartfridge

Systems and Software Engineering

squadaio

stock-market-sentiment-analysis

Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka