I have 8 years of work experience and working on advanced machine learning, neural network, NLP & Deep Learning projects. I have technical expertise in Convolutional Neural Networks, Recurrent Neural network, LSTM, Text analytics, Information Retrieval, Natural Language Processing, Scientific Spacy, Gensim, Named Entity Recognition, architecting Gene Regulatory Network Architecture designing for identifying candidate driver genes for cancer for Multiple Myeloma from MMRF, and currently developing a search engine to develop established AI products in the Healthcare using PyTorch & Keras. I got an award of full free registration to attend the conference and workshop track in ICML 2021, attended the Google Developer’s program, serving as a reviewer of a journal on AI & Deep Learning, attended the RegML summer program, Italy, and presented Attention is all you need paper to Portland State University, USA. I have contributed to real-world projects from multiple domain horizons & could transfer these skills in my work.
At present, I am using hypothesis testing, modelling, training, and interpreting predictive and statistical models in Machine and Deep learning, Bayesian statistical modeling. Overall I have end-to-end data science work experience from corpus collection from heterogeneous sources and data management till deployment and model retraining using pre-trained and custom-built models.