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Python, ML, AI, PyTorch, Data Mining, NLP

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BT

Bharat T.

Mid

Machine Learning Engineer

Summary
Technical Skills
Projects Worked On
Mid Level
Summary
  • Having 4+ years of experience in IT and comprehensive industry knowledge on Machine Learning, Artificial Intelligence, Statistical Modeling, Data Analysis, Predictive Analysis, Data Manipulation, Data Mining, Natural Language Processing.
  • Expertise in building scalable ML systems for real-time data analysis and decision-making.
  • Proficient in reinforcement learning, clustering, and recommendation systems.
Technical Skills

Languages: Python,

Packages: Scikit-Learn, Numpy, Pandas, NLTK, Matplotlib, Spacy ,Nltk, PyTorch, spacy, shap, lime, Statistical Analysis, Linear/Logistic Regression, PCA, Ensemble Trees, Random Forests, Clustering, NLP, Multilabel Text classification, Named Entity Recognization

Projects worked on

Sirion Autoextraction

Responsibilities:

  • Responsible for collecting relevant data, developing system models, prediction algorithms, solutions to prescriptive analytics problems, data mining techniques, and/ or econometric models.
  • Communicate the results with the operations team for taking the best decisions and Collect data needs and requirements by Interacting with the other departments.
  • Performed data cleaning using pandas ,numpy Python and Regex applied some business customized stop word removal, pattern replacement after performing multiple experiments and EDA experimented on multiple token split windows for tokenizing text of length more than 512 words
  • Experimented with multiple activation function for hidden units of bert like swish,gelu ,relu
  • Experimented with multiple pretrarained model like bert, albert, xlnet
  • Experimented with approaches like text translation and reverse translation for balancing skewed classes maintained data uniformity by checking similarity with overlapping category, text features of below certain length threshold with multiple techniques of text extraction like question ,ner for information retrieval
  • Developed service for auto extraction of contract documents using NLP classification, NER, and question answering models using state of art bert embedding .
  • Designed service for same using flask, pulsar, and python
  • Formed accuracy algorithm using fuzzy match for NER Model
  • Supervised model performance over time and updated when needed
  • Composed docker for seamless deployment of models
  • Updated old models, increased extraction efficiency by 70 percent

Environment: Python 3.x, Linux, Spark, Microsoft Excel, spacy, nltk, transformers, dash, plotly

Web application for training automation

Responsibilities:

  • Automated Nlp Training Process
  • Created Ui Trainer using Dash and python
  • Stored metadata using mongo db,

Automation of inventory management system

Responsibilities:

  • Responsible for developing and deploying risk-based decision tools and building knowledge-based systems to solve large scale computational problems
  • Communicate the results with the operations team for taking best decisions and Collect data needs and requirements by Interacting with the other departments
  • Worked on data cleaning and ensured data quality, consistency, integrity using Pandas, NumPy.
  • Used Statistical methods like missing value analysis, outlier detection feature selection for creating relevant data for machine learning model
  • Experimented and built predictive models including ensemble methods such as Question Answering CERTIFICATIONS Udacity Deep Learning Certification Udacity NLP Nanodegree Simplilearn Statstics Gradient boosting trees,extreme gradient boosting.
  • Reduced Dimension of data by merging multiple data together
  • Created Dashboard using sky wise slate
  • Created regression model for EDT of inventories using Xgboost
  • Delivered insights causing delay of part delivery, using feature weight interpretation
  • boosted part delivery by 30 percent

Sell forecasting of flight part

Responsibilities:

  • Responsible for developing and deploying time series based forecasting model
  • Communicate the results with the operations team for taking best decisions and Collect data needs and requirements by Interacting with the other departments
  • Worked on data cleaning and ensured data quality, consistency, integrity using Pandas, NumPy.
  • prepared data for sequence modeling
  • trained and deployed forecasting based LSTM model to forecast future sales of next one year
  • provisioned online training based on data drift and concept drift

Concession Classification

Responsibilities:

  • Collected multiple Datasets
  • Cleaned and preproceed Dataset using numpy pandas,regex
  • Ensured consistency and imntegrity of dataset
  • Created concession classification model using lstm and word2vec
  • Reduced human intervention up to 80 percent

Insurance reassignment classification

Responsibilities:

  • Developed consolidated Dataset from 35 DB Parquet files using spark on AWS EMR
  • Done extensive EDA on data using Python, seaborn, and pandas profiling
  • Selected features using forward elimination process and PCA
  • Created Reassignment Insurance Classifier using catboost
  • Deployed Model on Production Using Docker
  • analyzed feature causing reassignment of the claims

Trainee Performance Predictor

Responsibilities:

  • Curated Dataset from ILP Database
  • Created features using aggregation
  • Selected features using RFE
  • Created Model using Xgboost Classifier
  • Deployed model in production using flask and gunicorn
  • delivered several insights using feature interpretation like feature causing students to fail and areas of improvement.

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