HCL Technologies / Software Engineer (ML/AI)
I have built a strong foundation in Data science, Artificial intelligence and Machine Learning profile. And I am fond of Mathematics. I have been keep on exploring different fields in ML like NLP, GAN and Computer Vision. I am actively looking for job opportunities in Data Science, Machine Learning, and Artificial Inte
My future goal involved learning as much as I can, until I eventually take a leadership role by becoming expert in my field. I want to contribute towards the growth of your organization financially and culturally and makes it useful for better and advanced future for humanity.
Review design & implementation • Being able to independently implement machine learning algorithms paying attention to efficiency and accuracy. • Understand new requirements and come up with design for new features
Programming Language: Python, R, C++, Ruby Machine Learning Frameworks: Flask, Keras, pandas, sklearn, TensorFlow, Numpy
Performed Real time video analysis for classifying the animal images using transfer learning ● Extracted frames from the video using OpenCV library ● Used the pretrained Google Inception model for animal image classification ● Optimised the model using placeholder so that it will take less time for lengthy videos
Japanese Language I, Japanese Language II , Deep Learning, Mathematical Foundation of Data Science, Introduction to Data Analytics, Multivariate Data Analysis, Probability and Statistics, Series and Matrices,
Created a model to predict the probability of a person getting infected by covid-19 using CatBoost Algorithm ● Performed EDA on the dataset of 4000 people to get the active hotspots/clusters of COVID-19 across the country ● Developed a linear regression model using CatBoost open-source grading boosting library with an accuracy of 86.7%
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Predicting the probability of Customer Retention based on customer Interaction time and visiting frequency ● Implemented Decision Tree algorithm on a raw dataset of 1000 unique customers with 60% accuracy_score
Devised a Fashion Recommendation Engine for online e-commerce shopping of vintage cloth set ● Created a dataset of 500 images by using web scraping and by considering different events and whether categories
Performed Sentimental analysis by applying Different NLP techniques on balanced dataset of 10000 tweets ● Performed feature engineering techniques like Word Embedding and Locality Sensitive Hashing through KNN algorithm
Generated new categories of fashionable clothes by applying GAN algorithm on MNIST fashion dataset ● Introduced Gaussian Noise, to produce a wide variety of data, sampling from different places in the target distribution