Machine Learning Engineer
Experienced data scientist skilled in building data-intensive applications using predictive modeling, data processing, and Python, able to translate business goals into deliverables.
I'm Developer- specialized in Machine Learning.
Through my 4 years of my career I have real world exposer of projects, client interaction, team management, architecture designing. I am able to provide high quality services.
Open Source Project
Jan 2022 - Present
Machine Learning Engineer with huge experience in Python, and it's framework like FastAPI, Django, Tensorflow etc.
July 2019 - Jan 2022
Innovative and deadline-driven Data Scientist with 3 years of experience in Machine Learning.
Jan 2019 - June 2019
Completed 6 Months of internship Machine Learning.
2015 - 2019 | Computer Science
Acropolis Institute Of Technology And Research - AITR
Karnataka Vidya Niketan
Adarsh Shishu Vihar H. S. School
Create tool for automate virtualization solution, and providing PAAS service to customer. This tool will be used by customer to create virtual machine, and deploy application on it. This tool will also provide monitoring and troubleshooting service to customer. This tool will also provide AI/ML based price prediction to customer.
Scraped data for pricing strategies for laptops, desktops, and gaming devices and build an ML model for price prediction. Collected data from various E-Commerce and OEM websites across the globe.
Automate the ticketing tool, with the help of Machine Learning. Where technologies like NLP and Wikipedia trained ULMfit Fastai models are used to fine-tune our Ticket dataset and to find out the ticket and assigned person.
The goal is to analyze the growth of markets like refineries, stock, etc. with the help of user reviews, sentiment, and comments. By using Fastai and Keras with Facebook's Roberta, LSTM, ULMFit, etc. to fine-tune the model.
Create avatars for human faces for the portfolio. Trained the model using the images dataset of approximately 143,000 images of Humans and Cartoons. Many different architectures of Neural Networks, Deep Convolutional GANs (Style GAN), CNN, UNet to train the model.
Detection of steel sheet defects needs to find using Machine Learning, this automation approach leads the industry to high-quality steel production. Build the custom CNN model with U-Net and using Keras. This automation is run as a web app build with FastAPI.
On the way of spreading knowledge
I love teaching, so I teach on YouTube,
Medium.com, Blogs etc.