About:
Data Scientist with 3+ years of proven experience working in intersection of Machine Learning and Deep Learning (NLP). Worked in teams intersection, 15 to 23 team members and played a big role in providing data-driven insights for early-stage technology companies.
Tech Stack:
Python, Statistics, Machine Learning, Deep Learning, Natural Language Processing Computer Vision, MLOps, AWS
🔹Scikit-lear, Keras, PyTorch
🔹Classification | summarization | generation | Name Entity Recognition | BIO Tagging | Transformers | CNN | RNN | LSTM | BERT | BART
🔹Docker | MLflow | Grafana | wandb | Github Actions / CircleCi / Jenkins | Terraform | Paperspace | AWS
A comprehensive apply first learn from experts tutorial book of Deep Learning basics that covers the fundamental theory and algorithms with Python implementations. Covers Deep Learning in plain english.
Explore Object Classification, Object Detection and Object Segmentation. Also implement End to End Industry grade Objecet Detection Pipelines.
🔴 Playlist Outcome:
✔️ You will be ready to implement and integrate ML or DL models with own/company application.✔️ You will be ready to create ML or DL models using AWS Sagemaker for custom usecases.
🔴 Playlist Outcome:
✔️ You will be ready to implement and integrate AWS with own/company application.
🔴Free Resources:
✔️ All Code and Notebooks on demanded