kovitimi Goto Github PK

anomaly-detection

anomalydetection

This program detects abnormal events happening at Oil Wells (it monitors real time data from sensors in the wells)

asrdemos

Collection of machine learning demos for Automatic Speech Recognition

avast-ctu-cape-dataset

awesome-hebrew-nlp

:book: A curated list of resources for NLP (Natural Language Processing) for Hebrew

awesome-llm-cybersecurity-tools

A curated list of large language model tools for cybersecurity research.

awesome-malware-analysis

Defund the Police.

awesome-ml-for-cybersecurity

:octocat: Machine Learning for Cyber Security

bayesian_changepoint_detection

Methods to get the probability of a changepoint in a time series.

bayesianchangepoint

An implementation of Adams & MacKay 2007 "Bayesian Online Changepoint Detection"

biostat-578

A repository for all of my teaching material

blog

The repo for my blog posts

buildingmachinelearningsystemswithpython

Source Code for the book Building Machine Learning Systems with Python

capa

The FLARE team's open-source tool to identify capabilities in executable files.

copilot-for-security

Microsoft Copilot for Security is a generative AI-powered security solution that helps increase the efficiency and capabilities of defenders to improve security outcomes at machine speed and scale, while remaining compliant to responsible AI principles

coursera-dl

A script for downloading course material (video's, pdfs, quizzes, etc) from coursera.org

cs231n.github.io

Public facing notes page

customer_churn_analysis

In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from business relationship and the factors affecting the customer decisions. We are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.

cyner

Cyber Security concepts extracted from unstructured threat intelligence reports using Named Entity Recognition

data

APTnotes data

datasciencecoursera

Data Science Signature Track course materials

datasharing

The Leek group guide to data sharing

dl-nlp-readings

My Reading Lists of Deep Learning and Natural Language Processing

docker-stacks

Personalized docker images.

edx-downloader

example-get-started

Get started DVC project

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

exquisite-mlops-production

feast-dask

feast-pyspark