nikhilvenkatkumsetty Goto Github PK

-board-infinity

10-steps-to-become-a-data-scientist

📢 Ready to learn! you will learn 10 skills as data scientist:📚 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.

30-seconds-of-code

Short JavaScript code snippets for all your development needs

936153539-qq.com

crawling the GDELT dataset using its full text search API

ai-and-soft-computing

Basics of AI including PyPlot tutorials, Fuzzy Logic, Genetic Algorithms, Bayesian Networks, Perceptrons and NN's.

ai_curriculum

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford, MIT, UC Berkeley.

air-writing

:sunny: A CNN based framework for unistroke numeral recognition in air-writing.

analytixpro

analytixpro-index.html

angular-cli

CLI tool for Angular

approximate-mwm

artificial-intelligence-for-trading

Content for Udacity's AI in Trading NanoDegree.

artificial-intelligence-verzeo

artificial_intelligence

Assignments as a part of course CS480 Introduction to Artificial Intelligence

associated-press-reposts

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-iot-sensors-and-sites-using-machine-learning-approaches

attack-and-anomaly-detection-in-iot-sensors-in-iot-sites-using-machine-learning-approaches

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.

auction-dapp

awesome-industrial-machine-datasets

awesome-public-datasets

A topic-centric list of HQ open datasets. PR ☛☛☛

backdoor

Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and Privacy 2019.

basics_of_data_analysis

Basic Data Analysis and Visualisation using Python and its modules. And application of the concepts on some basic Machine Learning Algorithms.

bda_py_demos

Bayesian Data Analysis demos for Python

berty

Berty is a secure peer-to-peer messaging app that works with or without internet access, cellular data or trust in the network

bigquery-node-leaflet

Rendering GDELT data on a map using the Google Big Query API, Node, Express and Leaflet.

blogapp

Created with CodeSandbox

building-damage-detection-and-classification-using-deep-learning

building-recommender-systems-with-machine-learning-and-ai

Building Recommender Systems with Machine Learning and AI, published by Packt

c-

c-language-udemy

by Abdul Bari