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100days

100 days of algorithms

100daysmlcodingchallenge

120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

17_classification

17 Classification Algorithms using R

2021-workshop-rsit

RSIT Workshop (Uni Tübingen) 2021

3arabi

A translator for arabic chat

9problems

9front

aas

Code to accompany Advanced Analytics with Spark from O'Reilly Media

ab_test

A/B test for an e-commerce website

abot

Abot makes it easy and fun to build your own digital assistant, and we include everything you need to get started.

absinthe

Ἀψίνθιον — A highly integrated, advanced, extensible and cluster-ready server.

academic_advisory

Collected opinions and advice for academic programs focused on data science skills.

accumulo

Mirror of Apache Accumulo

acm-cheat-sheet

Acm Cheat Sheet

actions-on-google-nodejs

Client library for Actions on Google using Node.js

actionssdk-eliza-nodejs

Eliza Actions SDK sample for Actions on Google

actionssdk-say-number-nodejs

Say a number Actions SDK sample for Actions on Google

activemq

Mirror of Apache ActiveMQ

ada-2018-umd

ADA program at UMD in 2018 focused on TANF retention

ada-intro-python

ada-intro-sql

adam

A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.

advanced-r-solutions

Set of solutions for the Advanced R programming book

aerial-object-detector

To develop a object detection system for aerial data

agentnet

Deep Reinforcement Learning library for humans

aif360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

airline-sentiment-analysis--text-mining

Airline Industry is the top competetive industry where the customer satisfaction is very important for their business.I this modern world the better side of the industry depends on the understanding of your customer.With the growing social media it is very easy for people to know about the positives and negatives of a particular airline based on the twitter reviews.So it is very important to analyze your twitter data and find the expectation of the customer and know where you are lagging. In this process we extracted Data from the twitter for different airlines in United States and performed text mining in R and SAS to predict the sentiment of the data. Classified the negative sentiments into different category and compared among the airlines to give insights for the business development.

ajax-solr

A JavaScript framework for creating user interfaces to Solr.

alfred-workflows

:metal: A collection of Alfred 3 workflows that will rock your world