bhumikadhingra Goto Github PK

indian-sign-language

january-20

jobmatch

jupyter-installation

machine-learning-inception-public

minor_project-cv-ranking

mlalgorithms

Minimal and clean examples of machine learning algorithms implementations

mlr

Multiple Linear Regression

mlrpro

Resume Check with Machine Learning

mnist-and-opencv

obstructionremoval

[CVPR 2020] Learning to See Through Obstructions

odm

A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images.

oops

The only guidebook to object oriented programming in C++ you will ever need.

outlet_sales_prediction

Pre-processing, analysing and building models to predict Item_Outlet_Sales for Big_Mart dataset.

plot_without_code

I learnt python and developed this GUI which would help data analysts to instantly get an overview of the uploaded dataset...This is just a prototype.... Planning to make a website which would work similar to this GUI and will help in quick analysis of the data using graphs..... #firststep #trying #python #learn #first_step_towards_dataanalysis #dataset

recsys-music

Music/Songs Recommendation System

resume-job-description-matching

The purpose of this project was to defeat the current Application Tracking System used by most of the organization to filter out resumes. In order to achieve this goal I had to come up with a universal score which can help the applicant understand the current status of the match. The following steps were undertaken for this project 1) Job Descriptions were collected from Glass Door Web Site using Selenium as other scrappers failed 2) PDF resume parsing using PDF Miner 3) Creating a vector representation of each Job Description - Used word2Vec to create the vector in 300-dimensional vector space with each document represented as a list of word vectors 4) Given each word its required weights to counter few Job Description specific words to be dealt with - Used TFIDF score to get the word weights. 5) Important skill related words were given higher weights and overall mean of each Job description was obtained using the product for word vector and its TFIDF scores 6) Cosine Similarity was used get the similarities of the Job Description and the Resume 7) Various Natural Language Processing Techniques were identified to suggest on the improvements in the resume that could help increase the match score

resume-ranker

Reviews resumes and assigns a score based on percentile matching file content.

resume-ranking

Automatic system to rank resume's based on Job Description

resume-ranking-application

Resume Recommendation System for Recruiters

resume-scoring-using-nlp

The objective of the project is to create a Resume Scoring algorithm using Natural Language Processing. The algorithm will parse resumes one by one and will create a Candidate Profile based on the skills mentioned in the resume. A corpus is created using Sketch Engine, Wikipedia pages for various required skills (example : Machine Learning, Data Science, Software developer, Programming) are given as input, Sketch Engine creates a corpus from the useful data available on the given links. Word Embeddings are created from the corpus and these are used to match the skills in the candidate resume with the required skills. Finally, the candidate profile is built and plotted as a bar graph for better visualization.

resume_filtering

A resume filtering based on natural language processing

resume_job_matching

a machine learned solution with rich features for job matching

road-lane-detection

Using OpenCV to detect road lane lines

selendroid

Got task 3 for ATG Internship. Used selendroid for automation in mobile apps. This can be used in software testing also.

simplelinearregression

Created a simple linear regression program and generated rss and r-square value.

slr

Simple Linear Regression

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

subtitle-synchronization

teachable-machine-v1

Explore how machine learning works, live in the browser. No coding required.