1. Ashutosh Vishnoi
NLP | Deep learning | Machine learning
ashutosh.vishnoi.790@gmail.com +91-7985521330
linkedin.com/in/ashutosh-vishnoi-23a0299a github.com/ashuvishnoi
EDUCATION
07/2016 – 07/2020
B-Tech
IIIT Jabalpur
ECE
PERSONAL PROJECTS
News Headline to Related Headline Recommendation
Cosine Similarity and Overlap scores are used to get the similarity
between sentences pairs.
Word2Vec Embeddings are used to get embeddings.
Fact Vs Opinion Statement Classification
Prepare the dataset and labeled it to fact vs opinion stating that
the sentences containing value words are mostly opinion.
Trained LSTM using 5 epochs and binary cross entropy loss with
90% test set accuracy.
Documents Clustering
Used K-means clusering to cluster the docs after preprocessing of
text data.
Preprocessing includes tokenization of sentences and word2vec
model to get the feature vectors of words.
Digit Recognizer
Model to classify the numbers of images among lots of 28*28 pixels
pictures using 4-layers neural network after resizing and
normalizing the pixel values of the image.
I have used keras library to build the convolutional model to
recognize the digits from 0 to 9.
Made this project as a API Service on Flask.
WORK EXPERIENCE
06/2019 – Present
Machine learning Internship ( 6- Months )
Unfound.ai
Mumbai
AI based News and Digital Media Company
To work on NLP features and after projectification of whole
project deploy it on AWS Server with setup of Nginx and
Uwsgi server for robust performance.
To work on cutting edge NLP problems and comeup with a
decent solution with taking care of algorithm speed and
available space.
To make every nlp projects as API Service.
To productionize the ML services on cloud and maintain it
with debugging the issues coming up.
SKILLS
Natural language processing Deep learning
Machine learning AWS GIT
Python Programming Matlab C Programming
IOT
ACHIEVEMENTS
Silver level(4- stars) in problem solving at HACKERRANK
in python language.
Hackerearth HDFC bank ML hiring challenge
Built a banking behavioural scorecard for self-employed customers using
supervised learning model. I have used svm model to predict the binary
flags with 90% accuracy and secured 51th rank amoung 1000
participants.
CERTIFICATES
Machine learning
Stanford University
Deep learning Specialization
deep learning.ai
Introduction to Data Science in Python
University of Michigan
Applied Machine learning in Python
University of Michigan
Applied Text Mining in Python
University of Michigan
LANGUAGES
English
Full Professional Proficiency
Hindi
Full Professional Proficiency
Discipline
Responsibities
