Rizi M. Profile

UDFORSK

AJAX

JavaScript

Linux

PHP

Python

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$10 USD / time

navi mumbai,

india

$10 USD / time

Klokken er nu 9:55 AM her

Tilmeldt september 7, 2016

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Rizi M.

@rizimuj

0,0

(0 anmeldelser)

0,0

$10 USD / time

navi mumbai,

india

$10 USD / time

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Genansættelsesrate

WORDPRESS | PHP | MYSQL | JS | AJAX | BOOTSTRAP

Software Engineer with 5+ years of experience in delivering responsive websites and cutting-edge engineering solutions with a wide range of e-commerce application and technology skills. ▶ Extensive expertise in UI and WordPress, with custom plugin and theme development. ▶ Proven ability to leverage full-stack expertise to build interactive and user-centered website designs to scale. ▶ Proficient in - HTML/CSS/Bootstrap/JS/JQuery/AJAX - PHP/MySQL/Shopify/WordPress/Custom Themes/Plugins/Apps - Python/Django - PSD/Figma/... to pixel-perfect website conversion ▶ Good with emails and landing pages ▶ Can help with Web Design (Figma, Webflow, etc.)

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Erfaring

Upwork

jan. 2020 - Nuværende

Creating designs, wireframes, prototypes, etc. using tools like Figma, Webflow, etc. Converting designs into full-fledged responsive websites. Managing projects received from codeur platform (similar to upwork).

M7Admit

mar. 2020 - sep. 2020 (6 måneder, 1 dag)

Managing end-to-end student outreach and onboarding, mainly via Linkedin.

Corum8

maj 2020 - aug. 2020 (3 måneder, 1 dag)

Planning, developing and managing digital strategy to improve web presence and achieve business development goals. In addition, listing different products on various marketplaces and managing communication and sales.

Uddannelse

BE Computer

University of Mumbai,

India

2016 - 2020

(4 år)

Kvalifikationer

Godrej Infotech

2019

Developed a website using Java.

CMP Infotech

2018

Fundamental dot net using C#, ASP.

Automation Anywhere

2019

Developed Bots and Robotic Automation Processes using Automation Anywhere tool.

Publikationer

Diabetes Disease Prediction using Retina Scan

IJSRET

A new model is applied here in order to classify the data gathered from diabetic patients. For testing the model, Pima Indians Diabetes data set and synthetic data sets are being used. Experimental results proved that our approach performs better in terms of accuracy, sensitivity, specificity, classification time, training time, network complexity and computational time compared to conventional radial basis function neural network.