Mukul Rawat

WORK HISTORY

EDUCATION

ABOUT MUKUL RAWAT

Currently, as a Machine Learning graduate student at Northeastern University, I am learning about the intricacies of data-driven decision-making models and how we can systemize a multi-disciplinary approach to deploy a strategic framework and competitive strategies for information systems by leveraging problem-solving abilities using Machine learning and Deep Learning algorithms. I am also interested in analysing the fitted data and how we can better reinforce our decisions utilising the user-friendly results by leveraging data analytics and data visualisation tools and techniques. Machine Learning, Natural Language Processing and Computer Vision are my primary areas of interest. Machine Learning: K-means and Hierarchical Clustering, Lasso and Ridge Regression, Regularization, Kemel Methods, Decision Tree, Random Forest, SVM, Naive Bayes, Multiclass Classification, Logistic Regression, K-NN, Stochastic gradient Descent, Text Mining (TF-lDF, n-grams), Featurization, Ensemble Methods (Bootstrap, Bagging, Adaboost, Forward Stagewise Additive Modeling Gradient Boosting), Generalized Linear Model (Bernoulli Regression, Multinomial Logistic Regression, Poisson Regression), Markov Chain model 2. Deep Learning: LSTM, Encoder Decoder, Attention Model, Transformers, GANs, CNNs 3. Maths: Statistical methods (Maximum-likelihood estimation, Maximum-a-posteriori estimation, kernel density estimation, Hypothesis Testing, Bayesian Statistics, Monte Carlo simulation), Probability Theory, Time Series Analysis, Mathematical Modeling, Linear algebra, Optimization, Calculus 4. Proficient libraries: Scikit-Learn, Statsmodels, Numpy, Pandas, SciPy, Matplotlib, Seaborn, spaCy, PyTorch, Keras, HuggingFace, MLflow 5. Database : MySQL, PostgreSQL, Snowflake 6. Technologies: AWS(Ec2, S3, Redshift, Lambda, Kinesis, Sagemaker, Step Functions), Spark(Spark ML, Spark SQL, Spark-NLP), Flask, Streamlit, Heroku, Docker, Django Feel free to leave me a message at: R••••••@northeastern.edu