VIKAS SINGH KAVIYA's Projects
python implementation of andrew ng's machine learning assignment 2 (Regularized logistic regression)
python implementation of andrew ng's machine learning assignments
:book: A curated list of resources dedicated to Natural Language Processing (NLP)
Curated list of Machine Learning, NLP, Vision Project Ideas
This application developed to provide a tool for the different colleges to easily maintain the college bus information. It was designed using Java and backend was made using MySql.
Telecom company was losing its customers provided its users' dataset. I applied various ML algorithms, ROC curve and confusion matrix to identify users who will churn and what minimum offer to give them so they don't churn and company stays in profit.
DEVELOPED AN I.S.A WHICH PERFORMS BASIC ARITHMETIC AND LOGICAL FUNCTIONS.
multi-class-text-classification problem
I took Andrew Ng's Machine Learning course on Coursera and did the homework assigments... but, on my own in python because I love jupyter notebooks!
a Deep Learning based Speller
Library for fast text representation and classification.
Enhanced the real-time accuracy of Handwritten digits recognition using various ML modules embedded with Image Processing Techniques; obtained 97.7% accuracy on MNIST Dataset using 3 layered Neural Networks.
This problem asks to determine whether a loan will default.
Code and model files for the paper: "A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction" (AAAI-18).
MNIST Handwritten Digits Classification using 3 Layer Neural Net 98.7% Accuracy
Movie plots by genre tutorial at PyData Berlin 2016
Neural Language Correction implemented on Tensorflow
Task was to build a model which can classify different Notes (dollars) and cheques from various banks for a smart ATM camera
Curated list of project-based tutorials
A statistical machine translation (SMT)-based grammatical error correction system that makes use of neural network joint models (NNJM) and and character-level SMT for spelling correction.
Python wrapper for Stanford CoreNLP.
Computation using data flow graphs for scalable machine learning
The job was to predict if a passenger survived the sinking Titanic or not with the help of machine learning. Majority of the task was to analyze what sorts of passengers were likely to survive the tragedy through graphs, analytics tools and ML modules.
web scrapping using BeautifulSoup