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“This consultant was very fast to tell you he could do the job. We had a discussion where I ried to ask him what solution was best to quickly get ML in place. He then made code for that connection and API. Then he requested $100 for 3 lines of code and said he had worked too hard and did not want to do more work. It was clear he was just after a quick buck and then leave. They deliver one milestone and then they defect. Do not use this person he is not to trust.”Per F. 2 days ago
Relation between two temporal series.
“Requests were not met, but the freelancer still committed and tried to get the job done.”Lorenzo C. 2 months ago
Project for Laxya A. -- 2
“Great work . Always kept me in loop so that i can understand the dev process . Will consider him for future machine learning work .”Sidhi A. 3 months ago
paper implementation for a deep learning project
“Expert in deep learning . Done what was expected Clear communication.”Laxya A. 3 months ago
Project 26583629 has been deleted
“Fast, quick and exactly what i needed”Harry S. 3 months ago
Project 26163167 has been deleted
“very good”Ank Y. 4 months ago
Research and DevelopmentJan 2020
-Intern Under Professor Ganesh RamaKrishnan (Computer Science , IITB). -Research work on Optical Character recognition , Object Detection , Pose Detection . -Software Development -Tools Used: Python ,Tensorflow,OpenCV,Django ,Flask , Pytorch , R.
Software developmentJan 2019 - Nov 2019 (10 months)
Our team Worked on developing a Facial Recognition System . -Tools used- OpenCV , Python , Scikit Learn . We have Developed a State of the Facial recognition System with the help of Deep Learning . Extensive Data Collection has been done with massive web scraping and other publicly available records have been included. Attention based model has been trained on this dataset to present state of the Art Results.
B-Tech Computer Science2015 - 2019 (4 years)
Machine Learning (2019)Stanford /Coursera
Credentials : [login to view URL] This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
Data Engineering on Google Cloud Platform Specialization (2019)Google/Coursera
Credentials:[login to view URL] This five-course accelerated specialization is designed for data professionals who are responsible for designing, building, analyzing, and optimizing big data solutions. Through a combination of video lectures, quizzes, and hands-on labs, learners carried out serverless data analysis and productionize machine learning models.
Deep Learning Specialization (2020)DeepLearing.ai/Coursera
Credentials:[login to view URL] The Deep Learning Specialization is designed to prepare learners to participate in the development of cutting-edge AI technology. Through five interconnected courses, learners develop a profound knowledge of the hottest AI algorithms, mastering deep learning from its foundations (neural networks) to its industry applications
TensorFlow in Practice Specialization (2020)Deepplearning.ai/Coursera
Credientials:[login to view URL] TensorFlow: In Practice. The goal was to help you take the next steps, such as going deeper into understanding Machine Learning and the practice of understanding loss functions, optimizers and more, or perhaps you want to know more about neural networks and the different types of layers, from convolutions to recurrent or LSTM.
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