Satyanarayana Repala

Data Scientist familiar with gathering, cleaning and organizing data for use by technical and non-technical personnel. Advanced understanding of statistical, algebraic and other analytical techniques. Highly organized, motivated and diligent with significant background in ML, DL,GNN and NLP.

Machine Learning: statistical models , feature selection using correlation and data normalization, transforming data, hyper parameter tuning, deciding loss functions and optimizers, ensemble models , bagging and Gradient boosting methods,PCA(principle component analysis)

Deep Learning: CNN , activation function, different layers, , regularization techniques, data preparation, data preprocessing, training models and

NLP: NER(named entity recognition), sentiment analysis, word2vec,CBOW,SKIPGRAM,Glove, BERT, TFIDF, N-gram,Transformers, Fast text embedding(char embedding).

Validation Techniques : AUC (Area under curve analysis) / ROC ( receiver operating characteristic curve),confusion matrix, Precision, Recall, F1 score.

Project : personalized recommendations to the user based on user history and demographics, using Graph neural networks (deep graph library).

Project : detecting products,shelves and positions in Retail store, using deep neural network

Project: Extracting invoice parameters , invoice number, date,account number,vendor,ship,and remit addressed

Project: extract contact entities , person name, title and company name email address from email threads and populate these details into sales force contact management screen

Project: Build, person ,facemask detection and social distance monitoring Video analytics application and deployed in SHINOBI CCTV monitoring app, Live Detection and streaming

Project : Text classification , intention detection in email threads, is email contains complaint, feedback, query, compliment, information using LSTM models , SVM models

Project : user activity monitoring tool , deep neural network to monitor user system , how much time spent on which application, browser , command line, media player, excell

Project : text classification, monitor user screen and classify the amount of improper text present on screen , to measure how much a child is exposed to abuse,hatred on social media platforms

Neural Networks And Deep Learning: coursera.org/verify/SLTJ3EGKFWQQ