Since 2021, I have been working with Fractal Analytics as a Data Scientist and applying cutting edge ML technologies to solve real world problems and converting data to business achievements.
Here, I am working as an Individual contributer and working on projects involving Entity Matching and Time Series Forecasting.
Since 2020, I have been working with Amantya Technologies as a Data Scientist and applying cutting edge ML technologies to solve real world problems and converting data to business achievements.
Here, I am responsible for full end-to-end lifecycle development of Machine Learning and Deep Learning projects, from initial requirement gathering to design, coding, unit testing, optimising, documentation and integration & production deployment in AWS.
Since 2018, I have been working with Cognizant Technology Solutions as a software engineer in Digital Marketing domain, where I have been repeatedly recognized for developing innovative solutions and writing robust codes for high-volume businesses,and solving a wide range of problems.
Here, I am responsible for full end-to-end lifecycle development of marketing campaigns, from initial requirement gathering to design, coding, unit testing, optimising, documentation and integration & deployment using Adobe Campaign.
I worked with Ai Technology Systems(AITS) as a Machine Learning Engineer Intern ,
Here, I Contributed to an open source project “DNNCompiler” which is an alternative to Tensorflow but for low form-factor devices(micro-controllers) like Raspberry Pi etc.
Every small smart device has a micro controller fitted into it. We are trying to bring the power of Deep Learning to the micro-controllers using the “DNNCompiler”.
I've completed my,
B.Tech in Computer Science and Engineering from Haldia Institute of Technology, Haldia, during the period of 2014 - 2018 with 8.73 cgpa.
Class 12 from BSF Senior Secondary School Kadamtala, during the period of 2012 - 2014 with percentage of 89.80%.
Class 10 from Good Shepherd School Bagdogra, during the period of 2011 - 2012 with percentage of 85.14%.
Since Sept 2021, I have been working with Fractal Analytics as a Data Scientist and applying cutting edge ML technologies to solve real world problems and converting data to business achievements.
Here, I am working as an Individual contributer and working on projects involving Entity Matching and Time Series Forecasting.
Great! place to learn.
Since Oct 2020, I have been working with Amantya Technologies as a Data Scientist and applying cutting edge ML technologies to solve real world problems and converting data to business achievements.
Here, I am responsible for full end-to-end lifecycle development of Machine Learning and Deep Learning projects, from initial requirement gathering to design, coding, unit testing, optimising, documentation and integration & production deployment in AWS.
Great! place to learn.
Since July 2018, I have been working with Cognizant Technology Solutions as a software engineer in Digital Marketing domain, where I have been repeatedly recognized for developing innovative solutions and writing robust codes for high-volume businesses,and solving a wide range of problems.
Here, I am responsible for full end-to-end lifecycle development of marketing campaigns, from initial requirement gathering to design, coding, unit testing, optimising, documentation and integration & deployment using Adobe Campaign.
Great! place to learn.
Here, I worked as a Machine Learning Engineer Intern. I contributed to an open source project “DNNCompiler” which is an alternative to Tensorflow but for low form-factor devices(micro-controllers) like Raspberry Pi etc.
Every small smart device has a micro controller fitted into it. We are trying to bring the power of Deep Learning to the micro-controllers using the “DNNCompiler”.
Great! place to learn.
Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings). Techniques used : XGBoost, SVD, SVD++.
Read MoreCaption Generation is a challenging artificial intelligence problem where a textual description must be generated for a given photograph.It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right order.
Read MorePlague is an epidemic event caused by Bacteria. A group of senior scientists misplaced a package containing fatal plague bacteria during one of their trips. With no means of tracking where the package is, scientists are now trying to come up with a solution to stop the plague. This plague has 7 different strains that are unique for each continent. This strain is expanding rapidly in each continent.
The dataset contains escalations of the plague for all the seven strains. The dataset is a time series in which the training set contains the number of individuals that are infected by the plague over a defined period of time.
Objective : Predict the total number of people infected by the 7 different pathogens.
Metric : Minimize the difference between predicted and actual rating (RMSE/MSE)
Read MoreThis project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying. This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.
Read MoreStatement: It is a recommendation System based on Content based Clustering . Similar items are grouped or clustered using Pairwise Eucledean Distances.
Problem: Build a recommendation engine which suggests similar products to the given product in any e-commerce websites ex. Amazon.com, myntra.com etc.
Objective: The recommendation engine, uses information about 1,80,000 products and each product will have multiple features named 1.Title of the product 2.Brand of the product 3.Color of the product 4.Type of the product 5.Image of the apparel , etc...
Read MoreWe are here building a minimal version of self driving car. Here, we have a front camera view. This will transfer input to the computer. Then Deep Learning algorithm in computer predicts the steering angle to avoid all sorts of collisions. Predicting steering angle can be thought of as a regression problem. We will feed images to Convolutional Neural Network and the label will be the steering angle in that image. Model will learn the steering angle from the as per the turns in the image and will finally predicts steering angle for unknown images.
Objective : Our objective is to predict the correct steering angle from the given test image of the road. Here, our loss is Mean Squared Error(MSE). Our goal is to reduce the MSE error as low as possible.
Read MoreProblem Statemtent :Suggest the tags based on the content that was there in the question posted on Stackoverflow.
It is a Multi-label classification problem
Multi-label Classification: Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A question on Stackoverflow might be about any of C, Pointers, FileIO and/or memory-management at the same time or none of these.Micro-Averaged F1-Score (Mean F Score) : The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal.
The formula for the F1 score is:
F1 = 2 * (precision * recall) / (precision + recall)
In the multi-class and multi-label case, this is the weighted average of the F1 score of each class. Read MoreStatement : Classify the given genetic variations/mutations based on evidence from text- based clinical literature using Logistic Regression, Random Forest, TF-IDF and Feature Engineering.
Performance metric : Multi class log-loss , Confusion matrix
Read More
Caption Generation is a challenging artificial intelligence problem where a textual description must be generated for a given photograph.It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right order.
Deep learning methods have demonstrated state-of-the-art results on caption generation problems. What is most impressive about these methods is a single end-to-end model can be defined to predict a caption, given a photo, instead of requiring sophisticated data preparation or a pipeline of specifically designed models.
