Aditya Vikram Singh

Data Scientist | Machine Learning Engineer | Data Analyst |

Aditya Vikram Singh is a highly skilled and competent Data Scientist, Machine Learning Engineer, and Data Analyst with over 2 years of experience. He possesses expertise in a range of machine learning techniques including logistic regression, SVM, decision tree, random forest, GBDT, CNN, R

4.7

Years of Experience

Education

bml munjal university, lucknow public school, saraswati vidhya mandir

Companies

matrixcare, bytelearn, paralleldots, rdiger whrl gmbh, edgistify, applied ai course

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Experience

2022 - Present
matrixcare
Data Scientist
2021 - 2022
bytelearn
Data Analyst
2021 - 2021
paralleldots
Backend Developer
Developed and maintained APIs -> Developed and maintained 20+ APIs for the company's website and business team. The use of APIs by the business team (to get required data) benefited both the tech and business team because the earlier business team used to ask for data from the backend (tech) team. Query Optimization -> Optimized SQL queries for heavy load parts of the system, which improved website responsiveness. Designed Algorithm for Giving Proper Indexing -> Designed and implemented an algorithm that can give proper indexing (shelf number, row number, and stack layer) for products over shelves.
2020 - 2021
rdiger whrl gmbh
Data Scientist
ANPR: Automatic Number Plate Detection (Python, YOLO-V4, CRAFT, CRNN) -> Detecting the license plate number of vehicles of all European countries. Deployed the model on RPi4 and ODroid board. The accuracy of ANPR (deployed on RPi4) is 94% of accuracy. It can detect the all special character of German alphabets and other special characters of European countries. This ANPR is capable of recognizing the country of vehicle by looking at its license plate. Currently working on the premium version of this ANPR. Container Number Detection (Python, YOLO-V4, CRAFT, Classifier) -> Detecting the container number of shipping containers and deployed the model on RPi4 and ODroid board. The overall accuracy of this model is 95%.
2020 - 2020
edgistify
Data Scientist
1. Extracting Warehouse Availability/To-Let Information from Newspapers (Python, YOLO-V4, OpenCV, Pytesseract) -> Extracting the warehouse availability/to-let information from advertisement section of the newspapers. Used custom trained YOLOv4 to locate & find the box around the advertisement sections and then applied images-processing techniques like canny-edge detection, dilation, contours, etc. to find the boxes around each ad. Passed the cropped advertisement section in Pytesseract model for OCR and then applied the text processing techniques to extract the required data. 2. Extracting the Location (i.e. Lat & Long) of Warehouses from Google-Map (Python, YOLO-V4, OpenCV, Selenium) -> Extracting the exact location (i.e. latitude and longitude) of warehouses from Google-Map in a given region of India. Used custom trained YOLOv4 model with manually labeled dataset to locate & find the box around warehouse/s in image of Google-Map. Used Selenium to click at a specific location (obtained from YOLO), to get the location of warehouse from Google-Map and to move the map in a defined region. 3. Building the Warehouse Database (Python, BeautifulSoup, Selenium, Pandas) -> Created the database of the warehouse availability, warehouse details, customer details, agent details for all states of India from various websites.
2019 - 2020
applied ai course
Machine Learning Engineer
1. Toxic Comment Classification and Unintended Bias Reduction (DL/ML, Keras, TensorFlow, Scikit-Learn, Python) -> The dataset has toxic and non-toxic comments. Given a comment model must find the toxicity and reduce the unintended bias. Various ML and DL models (like logistic regression, random forest, Bi-directional GRUs, etc.) were used and then stacked the good performing models. It can be used in online discussion forums. -> Blog: https://medium.com/datadriveninvestor/jigsaw-unintended-bias-in-toxicity-classification-d9adf34307d3 2. Question-Answering System similar to Simple Search Engine (DL/ML, Keras, TensorFlow, Scikit-Learn, Python) -> In the dataset there are 10 answers for each question but only one of them is correct. For a given pair of question and answer model must predict whether it is correct or not. The best model was selected to calculate the MRR (mean reciprocal rank) on test data. Various DL/ML models (like logistic regression, random forest, Bi-directional GRUs, etc.) were used. -> Blog: https://medium.com/@singhadityastudy/simple-search-engine-9062a644da5c?source=friends_link&sk=a2ee7211f6b812a5b8f0039b4c9eb84d

Experience

103 Skills

Amazon Web Services (AWS)

Application Programming Interfaces (API)

architecture

Artificial Intelligence (AI)

Backend

BERT

Business Intelligence (BI)

C

Computer Vision

Convolutional Neural Networks (CNN)

Data Analysis

Data Analytics

Data Science

Data Scientist

Data Structures

Decision Trees

Deep Learning

Education

Flask

GitHub

Gradient Boosting

Heroku

Hypertext Transfer Protocol (HTTP)

Image Processing

Java

k-means clustering

Keras

LightGBM

Logistic Regression

Long Short-term Memory (LSTM)

Machine Learning

Machine Learning (ML)

Mathematics

Matlab

matlab

Matplotlib

Microsoft Excel

Microsoft Office

Microsoft PowerPoint

Microsoft Word

MySQL

Natural Language Processing (NLP)

Neural Networks

NLTK

NumPy

Object detection

OpenCV

optimization

Pandas

Pandas (Software)

Probability

Python

PyTorch

Pytorch

R

Random Forest

Recommender Systems

Recurrent Neural Networks (RNN)

Research Scientist

ResNet

RNN

Scikit

Scikit-Learn

SciPy

scipy

Seaborn

Search

Selenium

Software Engineer

SQL

Statistics

Supervised Learning

Support Vector Machine (SVM)

SVM

TensorFlow

Tensorflow

test

Unsupervised Learning

Vision

XGBoost

Education

2015 - 2019
bml munjal university
Bachelor of Technology - BTech
Mechanical Engineering
2012 - 2014
lucknow public school
Senior Secondary (XII)
2010 - 2012
saraswati vidhya mandir
High School (X)