Aug 2020 – Apr 2021 · 8 mos

Aug 2019 – Aug 2020 · 1 yr

Nov 2017 – Jul 2019 · 1 yr 8 mos

• Worked on several projects of Deep Learning and traditional Machine learning involving following techniques :
• 1) Image Classification & Object Detection : Convolution Neural Networks
• 2) Face swapping : Autoencoders
• 3) Speech2Text : Bidirectional LSTM
• 4) Image Captioning : CNN + LSTM
• 5) Text Mining : Entity extraction/Tagging
• 6) Clustering : Kmeans, Hierarchical
• 7) Optical Character Recognition
• 8) Classification Algorithms: Random Forest, XGBoost, SVM
• 9) Regression Analysis: Linear and Logistic Regression
• 10) Audio fingerprinting
• 11) Auto ML : H2O.ai , Auto-Keras
• Tools:
• 1) Python
• 2) R
• 3) Docker
• Frameworks:
• 1) Tensorflow, Keras
• 2) Sagemaker SDK
• 3) Mxnet, Gluon
• 4) Pytorch
• Model Optimizers:
• 1) TensorFlow Lite
• 2) Sagemaker Neo
• 3) TensorRT
• 4) OpenVINO: Intel's MO
• Edge Devices:
• 1) AWS Deeplens
• 2) Nvidia Jetson Nano
• 3) Acer aiSage
• 4) Hikivision IP Cameras
• Cloud Platform:
• 1) AWS
• 2) Google Cloud platform

Jul 2016 – Nov 2017 · 1 yr 4 mos · Gurgaon, India

• Experience in driving the development of real-world applications of large-scale Machine Learning and Data Mining delivering business value across various industries and verticals.
• Key Competency Areas:
•  Machine Learning
•  Linear and Logistic Regression
•  Decision Trees, Random Forest
•  SVM, KNN, K-means, Naïve Bayes
•  Text Mining, Sentiment analysis
•  Artificial Neural Networks
•  ARIMA Forecasting
•  Segmentation
•  Boosting Algorithms

Apr 2015 – Jul 2015 · 3 mos · Noida, Uttar Pradesh, India

Apr 2014 – Sep 2014 · 5 mos · Noida, Uttar Pradesh, India

Jan 2013 – Oct 2013 · 9 mos · India