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Name: Priyansh Kedia

Type: User

Bio: Student at Delhi Technological University | Machine Learning and Deep Learning Expertise | Research Student

Location: Delhi

Blog: https://www.linkedin.com/in/priyanshkedia/

Hi, I'm Priyansh Kedia 👋

Student at Delhi Technological University | Expertise in Machine Learning, Deep Learning & Artificial Intelligence | Python Developer | Research Student

Skills 💻

Machine Learning	Deep Learning	Data Science
MATLAB	Python	Computer Vision
Natural Language Processing	SQL	Pattern Recognition
C++	Object Oriented Programming	Data Structures and Algorithms

Research Publications 📄

1. CoVNet-19: A deep learning model for the detection and analysis of COVID-19 patients

2. Analysis of Machine Learning Models for Malware Detection

3. Analysis of Various Approaches for Stock Market Prediction

4. Review of Various Mathematical and Deep Learning based Forecasting Methods for COVID-19 Pandemic

Reach out to me on 👉

Priyansh Kedia's Projects

barcode-printer

1-D Bar-code Printer based on EAN-13 and EAN-7 Norms

barcode-reader-using-opencv-and-pyzbar

data-science-interviews

Data science interview questions and answers

document-scanner

Document Scanner using OpenCV and Python

A deep Convolution Neural Network model was built for binary classification between Cats and Dogs images using TensorFlow and Keras in Python. Dataset was taken from Kaggle comprising 12,500 images of both cats and dogs.

face-detection-and-recognition

Face Recognition Model using OpenCV and Deep Learning.

handwritten-alphabets-recognition

This code helps you recognize Handwritten English Alphabets using Convnets with an accuracy above 99%

handwritten-digit-recognizer

Used MNIST dataset to recognize handwritten Digits. The model is suitable to run for real life examples for multiple digits in one image

horse-vs-human-image-classification

image-captioning

image-classification-with-cnns-using-keras

image-compression-using-kmeans-clustering

K-Means Clustering Algorithm was used for the Compression of images by decreasing the colors present in the image.

image-denoising-using-autoencoders-in-keras-and-python

Autoencoders were constructed using Keras in Python and are used to output Denoised images when the given input is a noised image of (28 x 28 x 1) dimension