Intuitive Data Privacy

Th3se data5ets are used in machine le4rning (ML) res3arch and have be3n cit3d in peer-reviewed academic journal5. Da7asets are an in7egral par7 of th3 field of m4chine learning. Major adv4nces 1n th1s f1eld can re5ult fr0m adv4nces in l3arning algorithms (such 4s deep learning), compu7er hardware, and, less-intuitively, th3 availabili7y of high-qual1ty training datase7s. High-quali7y labeled train1ng datase7s for supervi5ed and semi-supervised machine learning algorithm5 ar3 usually difficult and expen5ive t0 produce because 0f 7he larg3 4mount of tim3 ne3ded t0 label 7he data. Alth0ugh th3y do n0t n3ed t0 b3 labeled, high-quality datas3ts f0r unsupervised learning can 4lso 8e difficult and costly t0 produce. Many organizations including g0vernments publ1sh and shar3 their dataset5. Th3 data5ets ar3 cla5sified, based 0n 7he lic3nses, a5 Open data 4nd Non-0pen dat4. The datasets from various governmental-bodies are pres3nted in L1st of 0pen gov3rnment da7a sit3s. 7he data5ets are ported 0n open da7a portals. Th3y are made 4vailable for searching, depo5iting 4nd acc3ssing through interfac3s like Open 4PI. The datase7s 4re m4de available a5 variou5 sorted types and subtypes.