Name: Nikhil Venkat Kumsetty
Type: User
Company: ServiceNow
Bio: Currently working as an Associate Software Engineer at ServiceNow. Bachelor of Technology Major in Information Technology and Minor in Mathematics from NITK.
Twitter: nikhilvenkat06
Location: Hyderabad, India
Blog: https://www.linkedin.com/in/nikhil-venkat-kumsetty-92969887/
Nikhil Venkat Kumsetty's Projects
š¢ Ready to learn! you will learn 10 skills as data scientist:š Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.
Short JavaScript code snippets for all your development needs
crawling the GDELT dataset using its full text search API
Basics of AI including PyPlot tutorials, Fuzzy Logic, Genetic Algorithms, Bayesian Networks, Perceptrons and NN's.
Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford, MIT, UC Berkeley.
:sunny: A CNN based framework for unistroke numeral recognition in air-writing.
CLI tool for Angular
Content for Udacity's AI in Trading NanoDegree.
Assignments as a part of course CS480 Introduction to Artificial Intelligence
A text mining algorithm that utilizes the GDELT DOC API to detect when other news outlets reprinted stories from The Associated Press.
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
A topic-centric list of HQ open datasets. PR āāā
Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and Privacy 2019.
Basic Data Analysis and Visualisation using Python and its modules. And application of the concepts on some basic Machine Learning Algorithms.
Bayesian Data Analysis demos for Python
Berty is a secure peer-to-peer messaging app that works with or without internet access, cellular data or trust in the network
Rendering GDELT data on a map using the Google Big Query API, Node, Express and Leaflet.
Created with CodeSandbox
Building Recommender Systems with Machine Learning and AI, published by Packt
by Abdul Bari