Work Experience

  RESPONSIBILITES

• • Prepare, maintain, and update the materials of projects named CRISPR/Case9

  • Build predictive models using various Machine Learning tools, for example, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn to predict the model like Fraud Detection and Regression problem datasets.

  • Develop algorithms using Natural Language Processing and Deep Learning models for predictive maintenance.

  • Design the existing algorithms, for instance, Decision Tree, Logistic Regression, Random Forest, Adaboost, and XGboost and execution to fit the models and make those generalised to boost the model performance.

  • Demonstrate knowledge owing to programming interface development and testing.

  • Prepare, maintain, and update the materials of projects named CRISPR/Case9 -Build predictive models using various Machine Learning tools, for example, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn to predict the model like Fraud Detection and Regression problem datasets. -Develop algorithms using Natural Language Processing and Deep Learning models for predictive maintenance. -Design the existing algorithms, for instance, Decision Tree, Logistic Regression, Random Forest, Adaboost, and XGboost and execution to fit the models and make those generalized to boost the model performance. -Demonstrate knowledge owing to programming interface development and testing.

• • Skills: Recurrent Neural Networks (RNN) · BERT · Convolutional Neural Networks (CNN) · SQL · Machine Learning · Artificial Intelligence (AI)

                                                                         RESPONSIBILITES

• • Building the model for the movie recommendation system using Pandas and K-Means Clustering.

  • Prepare dataset for the classification of sentimental analysis using Machine Learning and RNN.

  • Worked with various algorithms in ML such as Logistic Regression, Random Forest, Decision Tree, XGBoost for projects like spam classification.

  • Building the model for the movie recommendation system using Pandas and K-Means Clustering. - Prepare dataset for the classification of sentimental analysis using Machine Learning and RNN. - Worked with various algorithms in ML such as Logistic Regression, Random Forest, Decision Tree, XGBoost for projects like spam classification.

• • Skills: Flask · Encoder Decoder · Recurrent Neural Networks (RNN) · Clustering · Recommender Systems · Natural Language Processing (NLP) · Machine Learning