{"id":1008918,"date":"2023-09-13T11:00:00","date_gmt":"2023-09-13T18:00:00","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/"},"modified":"2025-02-13T02:05:05","modified_gmt":"2025-02-13T09:05:05","slug":"makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/","title":{"rendered":"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar"},"content":{"rendered":"\n<p>Yapay zekadaki (AI) makine \u00f6\u011frenimi modelleri, bilgisayarlar\u0131n verilerden \u00f6\u011frenmesini ve a\u00e7\u0131k programlama gerektirmeden tahminlerde veya yarg\u0131larda bulunmas\u0131n\u0131 sa\u011flar. Makine \u00f6\u011frenimi modelleri, h\u0131zla de\u011fi\u015fen teknoloji d\u00fcnyas\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7an geli\u015fmelerin arkas\u0131ndaki ilham kayna\u011f\u0131d\u0131r. Geleneksel programlama ba\u015far\u0131s\u0131z oldu\u011funda, bize karma\u015f\u0131k sorunlara dinamik bir \u00e7\u00f6z\u00fcm sunar.  <\/p>\n\n<p>Makine \u00f6\u011frenimi modelleri yapay zekan\u0131n kalbi ve ruhudur. Bu blogda makine \u00f6\u011frenimi modelleri, bunlar\u0131n bir\u00e7ok farkl\u0131 t\u00fcr\u00fc, ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131 ve \u00f6zel ihtiya\u00e7lar\u0131n\u0131z i\u00e7in en iyi modeli nas\u0131l se\u00e7ece\u011finiz hakk\u0131nda bilgi edinece\u011fiz. <\/p>\n\n<h2 class=\"wp-block-heading\">Makine \u00d6\u011frenimi Modeli Nedir?<\/h2>\n\n<p>Makine \u00f6\u011frenimi modeli, bilgisayarlar\u0131n karar vermek veya tahminlerde bulunmak i\u00e7in kulland\u0131\u011f\u0131 bir programd\u0131r. \u00d6rneklerden ve ge\u00e7mi\u015f verilerden \u00f6\u011frenerek olaylar\u0131 ba\u011f\u0131ms\u0131z olarak \u00e7\u00f6zer. <\/p>\n\n<p>Bir bilgisayara kedi ve k\u00f6pek resimlerini tan\u0131may\u0131 \u00f6\u011fretti\u011finizi d\u00fc\u015f\u00fcn\u00fcn. Ona bir s\u00fcr\u00fc kedi ve k\u00f6pek foto\u011fraf\u0131 g\u00f6sterip hangilerinin kedi hangilerinin k\u00f6pek oldu\u011funu s\u00f6yl\u00fcyorsunuz. Bilgisayar bu \u00f6rneklerden \u00f6\u011frenir ve kediler ile k\u00f6pekler aras\u0131ndaki farklar\u0131 tan\u0131maya ba\u015flar.  <\/p>\n\n<p>Yeterince \u00f6\u011frendikten sonra, ona yeni bir foto\u011fraf g\u00f6sterebilirsiniz ve o size bunun bir kedi mi yoksa k\u00f6pek mi oldu\u011funu s\u00f6yleyecektir. Bunu, e\u011fitim g\u00f6r\u00fcnt\u00fclerinden \u00f6\u011frendiklerini kullanarak ba\u015far\u0131r. <\/p>\n\n<p>Makine \u00d6\u011frenimi modelleri bir bilgisayar\u0131n beyni olarak hizmet eder. Bilgisayara bilgi verildi\u011finde bir \u015feyleri tahmin etmesine, s\u0131ralamas\u0131na veya karar vermesine yard\u0131mc\u0131 olan matematiksel veya algoritmik bir \u00e7er\u00e7evedir. Model eski bilgilere bakarak daha ak\u0131ll\u0131 hale gelir ve daha sonra bu bilgiyi daha \u00f6nce g\u00f6rmedi\u011fi yeni \u015feyler hakk\u0131nda tahminlerde bulunmak i\u00e7in kullanabilir.  <\/p>\n\n<h2 class=\"wp-block-heading\">Makine \u00d6\u011frenimi Algoritmas\u0131 Nedir?<\/h2>\n\n<p>Bir Makine \u00d6\u011frenimi (ML) algoritmas\u0131, bir makine \u00f6\u011frenimi modelinin kal\u0131plar\u0131 anlamak ve verilere dayal\u0131 tahminler veya yarg\u0131larda bulunmak i\u00e7in kulland\u0131\u011f\u0131 matematiksel ve istatistiksel kurallar ve prosed\u00fcrler toplulu\u011fudur.<\/p>\n\n<p>Makine \u00f6\u011frenimi algoritmalar\u0131, bilgisayarlar\u0131n bilgilerden bir \u015feyler \u00f6\u011frenmesine, kal\u0131plar bulmas\u0131na ve tahminler veya se\u00e7imler yapmas\u0131na yard\u0131mc\u0131 olur. Bu algoritmalar, makine \u00f6\u011frenimi modellerinin temelini olu\u015fturur. Bu modeller, \u00f6nemli bilgileri ortaya \u00e7\u0131karmak ve verilerden \u00f6\u011frendiklerine dayanarak g\u00f6revleri otomatik olarak ger\u00e7ekle\u015ftirmek i\u00e7in sekt\u00f6rler aras\u0131nda \u00e7e\u015fitli i\u015f t\u00fcrlerinde kullan\u0131lmaktad\u0131r.  <\/p>\n\n<h2 class=\"wp-block-heading\">ML Algoritmas\u0131 ve ML Modeli Aras\u0131ndaki Fark<\/h2>\n\n<p>Makine \u00f6\u011frenimi yolculu\u011funuza ba\u015flarken makine \u00f6\u011frenimi algoritmas\u0131 ile makine \u00f6\u011frenimi modeli aras\u0131ndaki fark\u0131 anlamak kritik \u00f6nem ta\u015f\u0131r.<\/p>\n\n<p>Makine \u00f6\u011frenimi algoritmas\u0131, makine \u00f6\u011frenimi sisteminizin yol g\u00f6sterici ilkelerine ve matematiksel prosed\u00fcrlerine benzer. Girdi verilerinizi i\u015fleyen, d\u00f6n\u00fc\u015ft\u00fcren ve en \u00f6nemlisi onlardan \u00f6\u011frenen bir hesaplama motoru olarak i\u015flev g\u00f6r\u00fcr. <\/p>\n\n<p>\u00d6te yandan, bir makine \u00f6\u011frenimi modeli, belirli bir veri k\u00fcmesine bir makine \u00f6\u011frenimi algoritmas\u0131 uyguland\u0131ktan sonra ortaya \u00e7\u0131kan ger\u00e7ek bir sonu\u00e7 veya temsildir. Algoritma taraf\u0131ndan s\u00f6z konusu veri k\u00fcmesinden toplanan bilgi veya \u00f6r\u00fcnt\u00fcleri i\u00e7erir. Ba\u015fka bir deyi\u015fle, \u00f6\u011frenme s\u00fcrecinin nihai sonucudur.  <\/p>\n\n<p>Bir makine \u00f6\u011frenimi algoritmas\u0131n\u0131, \u00f6\u011frenme s\u00fcrecini y\u00f6nlendiren bir yemek kitab\u0131 veya talimat koleksiyonu olarak d\u00fc\u015f\u00fcn\u00fcn. Bu, size bir yeme\u011fi nas\u0131l haz\u0131rlayaca\u011f\u0131n\u0131z\u0131 anlatan bir yemek kitab\u0131na sahip olmaya benzer. \u00d6te yandan bir makine \u00f6\u011frenimi modeli, bu form\u00fcl\u00fc takip etmenin sonucudur. Bitmi\u015f yeme\u011fe benzer.   <\/p>\n\n<h2 class=\"wp-block-heading\">Makine \u00d6\u011frenimi Modellerinin T\u00fcrleri<\/h2>\n\n<p>Makine \u00f6\u011frenimi, genel olarak \u00fc\u00e7 kategoriye ayr\u0131lan \u00e7ok \u00e7e\u015fitli model ve algoritmalar\u0131 i\u00e7erir: denetimli, denetimsiz ve peki\u015ftirmeli \u00f6\u011frenme. Bu kategorilerin her birinde \u00e7e\u015fitli alt kategoriler ve \u00f6zel modeller vard\u0131r. \u0130\u015fte pop\u00fcler makine \u00f6\u011frenimi modellerinin farkl\u0131 t\u00fcrlerine h\u0131zl\u0131 bir genel bak\u0131\u015f:  <\/p>\n\n<h3 class=\"wp-block-heading\">01. Denetimli Makine \u00d6\u011frenimi Modelleri<\/h3>\n\n<p>Denetimli \u00f6\u011frenme modeli, e\u011fitmek i\u00e7in etiketli verileri kullanan farkl\u0131 makine \u00f6\u011frenimi modellerinin belirli bir kategorisidir. Algoritma, girdi verilerini bilinen hedef etiketlerle e\u015fle\u015ftirerek denetimli \u00f6\u011frenmede tahminler veya yarg\u0131lar \u00fcretmeyi \u00f6\u011frenir. Bu modeller, giri\u015f \u00f6zelliklerine dayal\u0131 olarak bir sonucun tahmin edilmesini gerektiren g\u00f6revler i\u00e7in kullan\u0131l\u0131r. A\u015fa\u011f\u0131da birka\u00e7 pop\u00fcler denetimli makine \u00f6\u011frenimi modeli yer almaktad\u0131r:   <\/p>\n\n<ul>\n<li><strong>Do\u011frusal Regresyon: <\/strong>Do\u011frusal regresyon modeli, regresyon g\u00f6revlerinde s\u00fcrekli bir say\u0131sal \u00e7\u0131kt\u0131y\u0131 tahmin eder. S\u00fcrekli bir say\u0131sal \u00e7\u0131kt\u0131y\u0131 tahmin etmeniz gerekti\u011finde, do\u011frusal regresyon modellerini kullanabilirsiniz. Girdi de\u011fi\u015fkenleriniz ile hedef de\u011fi\u015fken aras\u0131ndaki en uygun do\u011frusal ba\u011flant\u0131y\u0131 tan\u0131mlar.  <\/li>\n\n\n\n<li><strong>Lojistik Regresyon: <\/strong>Lojistik regresyon, \u00e7\u0131kt\u0131 olarak ikili (evet\/hay\u0131r) bir se\u00e7im i\u00e7eren ikili s\u0131n\u0131fland\u0131rma g\u00f6revleri i\u00e7in kullan\u0131l\u0131r. Giri\u015f niteliklerinize dayanarak, ikili bir sonucun olas\u0131l\u0131\u011f\u0131n\u0131 hesaplar. <\/li>\n\n\n\n<li><strong>Karar A\u011fa\u00e7lar\u0131: <\/strong>Karar a\u011fa\u00e7lar\u0131 hem s\u0131n\u0131fland\u0131rma hem de regresyon modelleri i\u00e7in kullan\u0131l\u0131r. Her bir d\u00fc\u011f\u00fcm\u00fcn bir \u00f6zelli\u011fe dayal\u0131 bir karar\u0131 yans\u0131tt\u0131\u011f\u0131 ve yapraklar\u0131n nihai bir s\u0131n\u0131f etiketini veya say\u0131sal de\u011feri temsil etti\u011fi a\u011fa\u00e7 benzeri bir yap\u0131 olu\u015ftururlar. <\/li>\n\n\n\n<li><strong>Rastgele Orman:<\/strong> Rastgele orman, a\u015f\u0131r\u0131 uyumu azalt\u0131rken tahmin do\u011frulu\u011funu art\u0131rmak i\u00e7in \u00e7ok say\u0131da karar a\u011fac\u0131n\u0131 kar\u0131\u015ft\u0131ran bir topluluk \u00f6\u011frenme stratejisidir. \u00c7ok say\u0131da karar a\u011fac\u0131n\u0131 entegre eden bir topluluk \u00f6\u011frenme y\u00f6ntemidir. <\/li>\n\n\n\n<li><strong>Destek Vekt\u00f6r Makineleri (SVM): <\/strong>DVM, \u00f6zellik alan\u0131 s\u0131n\u0131flar\u0131n\u0131 b\u00f6lmek i\u00e7in optimum hiper d\u00fczlemi bulan s\u0131n\u0131fland\u0131rma i\u00e7in sofistike bir algoritmad\u0131r. \u0130kili ve \u00e7ok s\u0131n\u0131fl\u0131 verileri s\u0131n\u0131fland\u0131rabilir. <\/li>\n\n\n\n<li><strong>K-En Yak\u0131n Kom\u015fular (K-NN): <\/strong>K-NN temel ama m\u00fckemmel bir s\u0131n\u0131fland\u0131rma ve regresyon algoritmas\u0131d\u0131r. Veri noktan\u0131z\u0131n s\u0131n\u0131f\u0131n\u0131 veya de\u011ferini, e\u011fitim verilerindeki k-en yak\u0131n kom\u015fular\u0131n\u0131n \u00e7o\u011funluk s\u0131n\u0131f\u0131na veya ortalama de\u011ferine g\u00f6re belirler. <\/li>\n\n\n\n<li><strong>Naive Bayes: <\/strong>Naive Bayes, Bayes teoremine dayanan olas\u0131l\u0131ksal bir s\u0131n\u0131fland\u0131rma algoritmas\u0131d\u0131r. Spam alg\u0131lama ve duygu analizi gibi metin kategorizasyon g\u00f6revlerini yerine getirir. <\/li>\n\n\n\n<li><strong>Sinir A\u011flar\u0131:<\/strong> Konvol\u00fcsyonel sinir a\u011flar\u0131 (CNN&#8217;ler) ve tekrarlayan sinir a\u011flar\u0131 (RNN&#8217;ler) gibi derin \u00f6\u011frenme modelleri son derece uyarlanabilir denetimli modellerdir. Bu makine \u00f6\u011frenimi modellerini g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma ve do\u011fal dil i\u015fleme gibi \u00e7e\u015fitli denetimli \u00f6\u011frenme g\u00f6revleri i\u00e7in kullanabilirsiniz. <\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">02. Denetimsiz Makine \u00d6\u011frenimi Modelleri<\/h3>\n\n<p>Denetimsiz \u00f6\u011frenme, algoritman\u0131n etiketli \u00e7\u0131kt\u0131 \u00fcretmeden verilerdeki kal\u0131plar\u0131 ve yap\u0131lar\u0131 ara\u015ft\u0131rd\u0131\u011f\u0131 bir t\u00fcr makine \u00f6\u011frenmesidir. Bu y\u00f6ntemler, belirli etiketleri tahmin etmek yerine verilerdeki do\u011fal kal\u0131plar\u0131 veya korelasyonlar\u0131 bulmaya \u00e7al\u0131\u015f\u0131r. \u0130\u015fte bir dizi yayg\u0131n denetimsiz makine \u00f6\u011frenimi modeli:  <\/p>\n\n<ul>\n<li><strong>K-Means K\u00fcmeleme: <\/strong>K-ortalamalar, verileri benzerliklere g\u00f6re k\u00fcmelere ay\u0131ran pop\u00fcler bir k\u00fcmeleme y\u00f6ntemidir. Veri noktalar\u0131n\u0131 yinelemeli olarak en yak\u0131n k\u00fcme merkezine atayarak k\u00fcmeler i\u00e7indeki varyans\u0131 azaltmaya \u00e7al\u0131\u015f\u0131r. <\/li>\n\n\n\n<li><strong>Hiyerar\u015fik K\u00fcmeleme: <\/strong>Hiyerar\u015fik k\u00fcmeleme, a\u011fa\u00e7 benzeri bir k\u00fcme yap\u0131s\u0131 olan bir dendrogram olu\u015fturur. Veri noktalar\u0131 aras\u0131ndaki hiyerar\u015fik ili\u015fkileri tasvir edebilir. <\/li>\n\n\n\n<li><strong>Gauss Kar\u0131\u015f\u0131m Modelleri (GMM&#8217;ler): <\/strong>GMM&#8217;ler verileri temsil etmek i\u00e7in farkl\u0131 Gauss da\u011f\u0131l\u0131mlar\u0131n\u0131 birle\u015ftirir. K\u00fcmeleme ve yo\u011funluk tahmininde s\u0131kl\u0131kla kullan\u0131l\u0131rlar. <\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">03. Takviyeli Makine \u00d6\u011frenme Modelleri<\/h3>\n\n<p>Takviyeli \u00f6\u011frenme, bir ajan\u0131n \u00e7evresiyle etkile\u015fime girerek karar vermeyi \u00f6\u011frendi\u011fi makine \u00f6\u011freniminin bir alt k\u00fcmesidir. Temsilci, \u00f6d\u00fcller veya cezalar \u015feklinde girdi alarak zaman i\u00e7inde k\u00fcm\u00fclatif \u00f6d\u00fclleri optimize eden bir politika \u00f6\u011frenir. \u0130\u015fte pop\u00fcler peki\u015ftirmeli \u00f6\u011frenme modelleri ve algoritmalar\u0131na baz\u0131 \u00f6rnekler:  <\/p>\n\n<ul>\n<li><strong>Q-\u00d6\u011frenme: <\/strong>Q-\u00d6\u011frenme, arac\u0131lar\u0131n en iyi eylem-se\u00e7im politikas\u0131n\u0131 \u00f6\u011frenmelerine yard\u0131mc\u0131 olan yayg\u0131n bir modelsiz takviye \u00f6\u011frenme algoritmas\u0131d\u0131r. Her durum-eylem \u00e7ifti i\u00e7in beklenen k\u00fcm\u00fclatif \u00f6d\u00fclleri saklayan bir Q-tablosu tutar. <\/li>\n\n\n\n<li><strong>Derin Q-A\u011flar\u0131 (DQN): <\/strong>DQN, Q de\u011ferlerine yakla\u015fmak i\u00e7in derin sinir a\u011flar\u0131n\u0131 kullanan bir Q-\u00f6\u011frenme uzant\u0131s\u0131d\u0131r. Karma\u015f\u0131k g\u00f6revlerin \u00e7\u00f6z\u00fcm\u00fcnde etkili oldu\u011fu kan\u0131tlanm\u0131\u015ft\u0131r. <\/li>\n\n\n\n<li><strong>SARSA (Durum-Eylem-\u00d6d\u00fcl-Durum-Eylem): <\/strong>SARSA, Q-\u00f6\u011frenme gibi, model i\u00e7ermeyen bir takviye \u00f6\u011frenme algoritmas\u0131d\u0131r. Durum-eylem \u00e7iftleri i\u00e7in Q de\u011ferlerini tahmin ederek ve politika de\u011fi\u015fikliklerini kullanarak en iyi politikay\u0131 belirler. <\/li>\n<\/ul>\n\n<h2 class=\"wp-block-heading\">Makine \u00d6\u011frenimi Modellerinin Uygulamalar\u0131<\/h2>\n\n<p>Makine \u00f6\u011frenimi (ML) modelleri, verileri de\u011ferlendirme, tahminler \u00fcretme ve i\u015flemleri otomatikle\u015ftirme kapasiteleri nedeniyle \u00e7e\u015fitli i\u015fletmelerde ve alanlarda \u00e7ok say\u0131da uygulamaya sahiptir. \u0130\u015fte makine \u00f6\u011frenimi modellerinin nas\u0131l kullan\u0131ld\u0131\u011f\u0131na dair baz\u0131 \u00f6rnekler: <\/p>\n\n<h3 class=\"wp-block-heading\">01. G\u00f6r\u00fcnt\u00fc Tan\u0131ma ve Bilgisayarla G\u00f6rme<\/h3>\n\n<ul>\n<li><strong>Nesne Alg\u0131lama: <\/strong>Makine \u00f6\u011frenimi modelleri g\u00f6r\u00fcnt\u00fclerdeki veya videolardaki nesneleri tan\u0131yabilir ve bulabilir; bu da s\u00fcr\u00fcc\u00fcs\u00fcz ara\u00e7larda, g\u00f6zetimde ve sa\u011fl\u0131k hizmetlerinde yararl\u0131d\u0131r.<\/li>\n\n\n\n<li><strong>Y\u00fcz Tan\u0131ma: <\/strong>G\u00fcvenlik sistemlerinde ve mobil cihazlarda yayg\u0131n olarak kullan\u0131lan bireylerin y\u00fczlerini tan\u0131ma ve onaylama.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">02. Do\u011fal Dil \u0130\u015fleme (NLP)<\/h3>\n\n<ul>\n<li><strong>Duygu Analizi: <\/strong>Duygu analizi, metin verilerinin tonunu (olumlu, olumsuz veya n\u00f6tr) bulma s\u00fcrecidir. Bu genellikle sosyal medyay\u0131 izlemek ve m\u00fc\u015fteri yorumlar\u0131n\u0131 analiz etmek i\u00e7in kullan\u0131l\u0131r. <\/li>\n\n\n\n<li><strong>Dil \u00c7evirisi: <\/strong>Google Translate gibi ara\u00e7larda g\u00f6r\u00fcld\u00fc\u011f\u00fc gibi, metni bir dilden di\u011ferine \u00e7evirme.<\/li>\n\n\n\n<li><strong>Metin Olu\u015fturma: <\/strong>Bir ki\u015fi yazm\u0131\u015f gibi g\u00f6r\u00fcnen metin olu\u015fturma. Bu, sohbet robotlar\u0131, i\u00e7erik \u00fcretimi ve sanal yard\u0131mc\u0131lar i\u00e7in kullan\u0131\u015fl\u0131d\u0131r. <\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">03. \u00d6neri Sistemleri<\/h3>\n\n<ul>\n<li><strong>\u0130\u00e7erik \u00d6nerileri: <\/strong>M\u00fc\u015fterilere \u00f6nceki davran\u0131\u015flar\u0131na ve tercihlerine g\u00f6re \u00fcr\u00fcn, film, m\u00fczik veya makale \u00f6nerme (\u00f6r. Netflix, Amazon).<\/li>\n\n\n\n<li><strong>Ki\u015fiselle\u015ftirilmi\u015f Pazarlama:<\/strong> Kullan\u0131c\u0131lara ilgi alanlar\u0131na g\u00f6re hedeflenmi\u015f reklamlar ve i\u00e7erikler sunmak.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">04. Sa\u011fl\u0131k Hizmetleri<\/h3>\n\n<ul>\n<li><strong>Hastal\u0131k Te\u015fhisi: <\/strong>T\u0131bbi resimler (\u00f6rn. X-\u0131\u015f\u0131nlar\u0131, MRI&#8217;lar) ve hasta verilerini kullanarak t\u0131p uzmanlar\u0131na hastal\u0131k te\u015fhisi konusunda yard\u0131mc\u0131 olmak.<\/li>\n\n\n\n<li><strong>\u0130la\u00e7 Ke\u015ffi: <\/strong>Olas\u0131 ila\u00e7 adaylar\u0131n\u0131 ve bunlar\u0131n belirli hastal\u0131klar\u0131n tedavisindeki yararl\u0131l\u0131\u011f\u0131n\u0131 tahmin etmek ila\u00e7 ke\u015ffi olarak bilinir.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">05. Finans<\/h3>\n\n<ul>\n<li><strong>Kredi Puanlamas\u0131: <\/strong>Kredi kabul\u00fcne karar vermek i\u00e7in bir ki\u015finin veya kurulu\u015fun kredibilitesinin de\u011ferlendirilmesi.<\/li>\n\n\n\n<li><strong>Algoritmik Ticaret: <\/strong>Piyasa verilerine dayanarak, ger\u00e7ek zamanl\u0131 olarak ticaret hakk\u0131nda kararlar vermek.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">06. Makine \u00d6\u011frenimi Modelleri ile Doland\u0131r\u0131c\u0131l\u0131k Tespiti<\/h3>\n\n<ul>\n<li><strong>Kredi Kart\u0131 Doland\u0131r\u0131c\u0131l\u0131k Tespiti:<\/strong> \u00d6nceki verileri ve harcama modellerini kullanarak hileli i\u015flemlerin tan\u0131mlanmas\u0131.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">07. Otonom Ara\u00e7lar<\/h3>\n\n<ul>\n<li><strong>Kendi Kendine Giden Arabalar: <\/strong>Makine \u00f6\u011frenimi modelleri, nas\u0131l s\u00fcr\u00fclece\u011fine karar vermek i\u00e7in sens\u00f6r verilerini analiz ederek verimlilik ve g\u00fcvenlik sa\u011flar.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">08. Makine \u00d6\u011frenimi Modelleri ile E\u011fitim<\/h3>\n\n<ul>\n<li><strong>Ki\u015fiselle\u015ftirilmi\u015f \u00d6\u011frenme: <\/strong>\u00d6\u011fretim i\u00e7eri\u011finin her \u00f6\u011frencinin gereksinimlerine ve yeteneklerine \u00f6zel olarak haz\u0131rlanmas\u0131.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">09. \u00c7evresel \u0130zleme<\/h3>\n\n<ul>\n<li><strong>\u0130klim modellemesi: <\/strong>\u0130klim de\u011fi\u015fikli\u011fini analiz etme ve hava durumu modellerini tahmin etme.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading\">10. Makine \u00d6\u011frenimi Modelleri ile G\u00fcvenlik<\/h3>\n\n<ul>\n<li><strong>\u0130zinsiz Giri\u015f Tespiti: <\/strong>Siber sald\u0131r\u0131lar\u0131 tespit etmek ve durdurmak i\u00e7in ola\u011fand\u0131\u015f\u0131 a\u011f davran\u0131\u015flar\u0131n\u0131 tespit etme.<\/li>\n<\/ul>\n\n<h2 class=\"wp-block-heading\">QuestionPro ile Makine \u00d6\u011frenimi Modellerini Geli\u015ftirme<\/h2>\n\n<p>QuestionPro, i\u015fletmelerin hedef kitlelerinden \u00f6nemli geri bildirimler, i\u00e7g\u00f6r\u00fcler ve veriler toplamak i\u00e7in anketler tasarlamas\u0131na, da\u011f\u0131tmas\u0131na ve analiz etmesine yard\u0131mc\u0131 olan bir anket yaz\u0131l\u0131m\u0131 platformudur. Platform, makine \u00f6\u011frenimi modellerinin \u00e7e\u015fitli \u015fekillerde olu\u015fturulmas\u0131na ve geli\u015ftirilmesine yard\u0131mc\u0131 olabilir: <\/p>\n\n<ul>\n<li><h3>Veri Toplama<\/h3><\/li>\n<\/ul>\n\n<p>Kat\u0131l\u0131mc\u0131lardan yap\u0131land\u0131r\u0131lm\u0131\u015f veri toplamak \u00fczere anketler olu\u015fturmak ve da\u011f\u0131tmak i\u00e7in QuestionPro&#8217;yu kullanabilirsiniz. Bu veriler makine \u00f6\u011frenimi modellerini e\u011fitmek i\u00e7in kullan\u0131labilir. <\/p>\n\n<p>\u00d6rne\u011fin, duyarl\u0131l\u0131k analizi, \u00f6neri sistemleri veya m\u00fc\u015fteri segmentasyonu modellerini e\u011fitmek i\u00e7in m\u00fc\u015fteri yorumlar\u0131n\u0131, \u00fcr\u00fcn derecelendirmelerini veya kullan\u0131c\u0131 tercihlerini toplayabilirsiniz.<\/p>\n\n<ul>\n<li><h3>Tasar\u0131m \u00d6zellikleri<\/h3><\/li>\n<\/ul>\n\n<p>Makine \u00f6\u011frenimi modelleri, tahminler veya s\u0131n\u0131fland\u0131rmalar olu\u015fturmak i\u00e7in ilgili \u00f6zelliklere (de\u011fi\u015fkenlere) ihtiya\u00e7 duyar. Anket verileri s\u0131kl\u0131kla makine \u00f6\u011freniminde kullan\u0131labilecek \u00f6nemli bilgiler i\u00e7erir. Modelleme \u00e7al\u0131\u015fman\u0131z i\u00e7in gerekli olan belirli nitelikleri veya \u00f6zellikleri yakalayan anketler geli\u015ftirmek i\u00e7in QuestionPro&#8217;yu kullanabilirsiniz.  <\/p>\n\n<p>\u00d6rne\u011fin, bir m\u00fc\u015fteri memnuniyeti anketinde ya\u015f, cinsiyet, co\u011frafya ve sat\u0131n alma ge\u00e7mi\u015fi gibi verileri toplayabilir ve bunlar\u0131 tahmine dayal\u0131 modeller olu\u015fturmak i\u00e7in kullanabilirsiniz.<\/p>\n\n<ul>\n<li><h3>A\/B Testi<\/h3><\/li>\n<\/ul>\n\n<p>\u00c7e\u015fitli model ayarlamalar\u0131n\u0131n veya m\u00fcdahalelerinin etkinli\u011fini de\u011ferlendirmek \u00fczere A\/B testleri tasarlamak ve \u00e7al\u0131\u015ft\u0131rmak i\u00e7in QuestionPro&#8217;yu kullanabilirsiniz. Bu bilgiler ML modellerinin iyile\u015ftirilmesi ve optimize edilmesinde olduk\u00e7a faydal\u0131 olabilir. <\/p>\n\n<ul>\n<li><h3>S\u00fcrekli \u0130yile\u015ftirme<\/h3><\/li>\n<\/ul>\n\n<p>Kurulu\u015flar, anketler yaparak ve d\u00fczenli olarak yeni veriler toplayarak makine \u00f6\u011frenimi modellerini s\u00fcrekli olarak g\u00fcncelleyebilir ve geli\u015ftirebilir. Yeni veriler elde edildik\u00e7e, do\u011fruluk ve alaka d\u00fczeyini korurken g\u00fcncel kalmak i\u00e7in modeller yeniden e\u011fitilebilir. <\/p>\n\n<ul>\n<li><h3>Ki\u015fiselle\u015ftirme ve segmentasyon<\/h3><\/li>\n<\/ul>\n\n<p>Anket verilerini kullanarak hedef kitlenizi se\u00e7imlerine, eylemlerine veya demografik \u00f6zelliklerine g\u00f6re kategorilere ay\u0131rabilirsiniz. Makine \u00f6\u011frenimi odakl\u0131 \u00f6neri sistemleri ve hedefli reklamc\u0131l\u0131k daha sonra kullan\u0131c\u0131 deneyimini veya pazarlama faaliyetlerini ki\u015fiselle\u015ftirmek i\u00e7in bu segmentleri kullanabilir ve b\u00f6ylece etkinliklerini art\u0131rabilir. <\/p>\n\n<p>Ara\u015ft\u0131rmalar\u0131n\u0131z\u0131 h\u0131zland\u0131rmaya ve veriye dayal\u0131 kararlar almaya haz\u0131r m\u0131s\u0131n\u0131z? Daha ak\u0131ll\u0131 veriler toplamak, analiz etmek ve bunlara g\u00f6re hareket etmek i\u00e7in \u015fimdi ba\u015flay\u0131n. <\/p>\n\n<p><\/p><p style=\"text-align: center;\"><a href=\"https:\/\/www.questionpro.com\/research-edition-survey-software\/\" target=\"_blank\" rel=\"noopener\">\n  <button>DAHA FAZLA BI\u0307LGI\u0307 EDI\u0307NI\u0307N<\/button>\n<\/a>       <a href=\"https:\/\/www.questionpro.com\/a\/showEntry.do?classID=1053&#038;sourceRef=blog\" target=\"_blank\" rel=\"noopener\">\n  <button>\u00dcCRETS\u0130Z DENEME<\/button>\n<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Yapay zekadaki (AI) makine \u00f6\u011frenimi modelleri, bilgisayarlar\u0131n verilerden \u00f6\u011frenmesini ve a\u00e7\u0131k programlama gerektirmeden tahminlerde veya yarg\u0131larda bulunmas\u0131n\u0131 sa\u011flar. Makine \u00f6\u011frenimi [&hellip;]<\/p>\n","protected":false},"author":80,"featured_media":814912,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[1114],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenimi modelleri, verilerden tahmin yapan veya karar veren makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 kullan\u0131r. Farkl\u0131 t\u00fcrlerini ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ke\u015ffedin.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar\" \/>\n<meta property=\"og:description\" content=\"Makine \u00f6\u011frenimi modelleri, verilerden tahmin yapan veya karar veren makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 kullan\u0131r. Farkl\u0131 t\u00fcrlerini ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ke\u015ffedin.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/\" \/>\n<meta property=\"og:site_name\" content=\"QuestionPro\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/questionpro\" \/>\n<meta property=\"article:published_time\" content=\"2023-09-13T18:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-02-13T09:05:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2023\/09\/ML-Models.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1750\" \/>\n\t<meta property=\"og:image:height\" content=\"1045\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"QuestionPro Collaborators\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@questionpro\" \/>\n<meta name=\"twitter:site\" content=\"@questionpro\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"QuestionPro Collaborators\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/\"},\"author\":{\"name\":\"QuestionPro Collaborators\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/person\/7aa2dda02c16e540da9fb962ee929652\"},\"headline\":\"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar\",\"datePublished\":\"2023-09-13T18:00:00+00:00\",\"dateModified\":\"2025-02-13T09:05:05+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/\"},\"wordCount\":2254,\"publisher\":{\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#organization\"},\"articleSection\":[\"Pazar Ara\u015ft\u0131rmas\u0131\"],\"inLanguage\":\"tr\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/\",\"url\":\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/\",\"name\":\"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar\",\"isPartOf\":{\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#website\"},\"datePublished\":\"2023-09-13T18:00:00+00:00\",\"dateModified\":\"2025-02-13T09:05:05+00:00\",\"description\":\"Makine \u00f6\u011frenimi modelleri, verilerden tahmin yapan veya karar veren makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 kullan\u0131r. Farkl\u0131 t\u00fcrlerini ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ke\u015ffedin.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Ev\",\"item\":\"https:\/\/www.questionpro.com\/blog\/tr\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uncategorized @tr\",\"item\":\"https:\/\/www.questionpro.com\/blog\/tr\/category\/uncategorized-tr\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#website\",\"url\":\"https:\/\/www.questionpro.com\/blog\/tr\/\",\"name\":\"QuestionPro\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.questionpro.com\/blog\/tr\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"tr\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#organization\",\"name\":\"QuestionPro\",\"url\":\"https:\/\/www.questionpro.com\/blog\/tr\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2022\/10\/questionpro-logo.svg\",\"contentUrl\":\"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2022\/10\/questionpro-logo.svg\",\"caption\":\"QuestionPro\"},\"image\":{\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/questionpro\",\"https:\/\/twitter.com\/questionpro\",\"https:\/\/www.linkedin.com\/company\/questionpro\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/person\/7aa2dda02c16e540da9fb962ee929652\",\"name\":\"QuestionPro Collaborators\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/7c5e4f557ac4d597814687054d6305bc?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/7c5e4f557ac4d597814687054d6305bc?s=96&d=mm&r=g\",\"caption\":\"QuestionPro Collaborators\"},\"description\":\"Worldwide team of Content Creation specialists focusing on Research, CX, Workforce, Audience and Education.\",\"url\":\"https:\/\/www.questionpro.com\/blog\/tr\/author\/aldro\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar","description":"Makine \u00f6\u011frenimi modelleri, verilerden tahmin yapan veya karar veren makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 kullan\u0131r. Farkl\u0131 t\u00fcrlerini ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ke\u015ffedin.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/","og_locale":"tr_TR","og_type":"article","og_title":"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar","og_description":"Makine \u00f6\u011frenimi modelleri, verilerden tahmin yapan veya karar veren makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 kullan\u0131r. Farkl\u0131 t\u00fcrlerini ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ke\u015ffedin.","og_url":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/","og_site_name":"QuestionPro","article_publisher":"https:\/\/www.facebook.com\/questionpro","article_published_time":"2023-09-13T18:00:00+00:00","article_modified_time":"2025-02-13T09:05:05+00:00","og_image":[{"width":1750,"height":1045,"url":"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2023\/09\/ML-Models.jpg","type":"image\/jpeg"}],"author":"QuestionPro Collaborators","twitter_card":"summary_large_image","twitter_creator":"@questionpro","twitter_site":"@questionpro","twitter_misc":{"Yazan:":"QuestionPro Collaborators","Tahmini okuma s\u00fcresi":"11 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/#article","isPartOf":{"@id":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/"},"author":{"name":"QuestionPro Collaborators","@id":"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/person\/7aa2dda02c16e540da9fb962ee929652"},"headline":"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar","datePublished":"2023-09-13T18:00:00+00:00","dateModified":"2025-02-13T09:05:05+00:00","mainEntityOfPage":{"@id":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/"},"wordCount":2254,"publisher":{"@id":"https:\/\/www.questionpro.com\/blog\/tr\/#organization"},"articleSection":["Pazar Ara\u015ft\u0131rmas\u0131"],"inLanguage":"tr"},{"@type":"WebPage","@id":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/","url":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/","name":"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar","isPartOf":{"@id":"https:\/\/www.questionpro.com\/blog\/tr\/#website"},"datePublished":"2023-09-13T18:00:00+00:00","dateModified":"2025-02-13T09:05:05+00:00","description":"Makine \u00f6\u011frenimi modelleri, verilerden tahmin yapan veya karar veren makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 kullan\u0131r. Farkl\u0131 t\u00fcrlerini ve ger\u00e7ek d\u00fcnyadaki uygulamalar\u0131n\u0131 ke\u015ffedin.","breadcrumb":{"@id":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.questionpro.com\/blog\/tr\/makine-ogrenimi-modelleri-ne-olduklari-turleri-uygulamalar\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Ev","item":"https:\/\/www.questionpro.com\/blog\/tr\/"},{"@type":"ListItem","position":2,"name":"Uncategorized @tr","item":"https:\/\/www.questionpro.com\/blog\/tr\/category\/uncategorized-tr\/"},{"@type":"ListItem","position":3,"name":"Makine \u00d6\u011frenimi Modelleri: Ne Olduklar\u0131, T\u00fcrleri + Uygulamalar"}]},{"@type":"WebSite","@id":"https:\/\/www.questionpro.com\/blog\/tr\/#website","url":"https:\/\/www.questionpro.com\/blog\/tr\/","name":"QuestionPro","description":"","publisher":{"@id":"https:\/\/www.questionpro.com\/blog\/tr\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.questionpro.com\/blog\/tr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"tr"},{"@type":"Organization","@id":"https:\/\/www.questionpro.com\/blog\/tr\/#organization","name":"QuestionPro","url":"https:\/\/www.questionpro.com\/blog\/tr\/","logo":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/logo\/image\/","url":"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2022\/10\/questionpro-logo.svg","contentUrl":"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2022\/10\/questionpro-logo.svg","caption":"QuestionPro"},"image":{"@id":"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/questionpro","https:\/\/twitter.com\/questionpro","https:\/\/www.linkedin.com\/company\/questionpro\/"]},{"@type":"Person","@id":"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/person\/7aa2dda02c16e540da9fb962ee929652","name":"QuestionPro Collaborators","image":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/www.questionpro.com\/blog\/tr\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/7c5e4f557ac4d597814687054d6305bc?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/7c5e4f557ac4d597814687054d6305bc?s=96&d=mm&r=g","caption":"QuestionPro Collaborators"},"description":"Worldwide team of Content Creation specialists focusing on Research, CX, Workforce, Audience and Education.","url":"https:\/\/www.questionpro.com\/blog\/tr\/author\/aldro\/"}]}},"featured_image_src":"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2023\/09\/ML-Models.jpg","featured_image_src_square":"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2023\/09\/ML-Models.jpg","author_info":{"display_name":"QuestionPro Collaborators","author_link":"https:\/\/www.questionpro.com\/blog\/tr\/author\/aldro\/"},"_links":{"self":[{"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/posts\/1008918"}],"collection":[{"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/users\/80"}],"replies":[{"embeddable":true,"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/comments?post=1008918"}],"version-history":[{"count":1,"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/posts\/1008918\/revisions"}],"predecessor-version":[{"id":1008924,"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/posts\/1008918\/revisions\/1008924"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/media\/814912"}],"wp:attachment":[{"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/media?parent=1008918"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/categories?post=1008918"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.questionpro.com\/blog\/tr\/wp-json\/wp\/v2\/tags?post=1008918"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}