Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu<\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<p>Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu<\/p>\n<p>Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. Bu makalede, makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131 farkl\u0131 kategorilere ay\u0131rmay\u0131 ve her kategorinin \u00f6zelliklerini ayr\u0131nt\u0131l\u0131 olarak ele almay\u0131 ama\u00e7l\u0131yoruz.<\/p>\n<h2>\u00d6\u011frenme T\u00fcr\u00fcne G\u00f6re Kategorizasyon<\/h2>\n<p>Makine \u00f6\u011frenmesi algoritmalar\u0131, \u00f6ncelikle nas\u0131l \u00f6\u011frendiklerine g\u00f6re s\u0131n\u0131fland\u0131r\u0131labilir. Bu a\u00e7\u0131dan \u00fc\u00e7 ana kategori vard\u0131r: <\/p>\n<h3>1. Denetimli \u00d6\u011frenme (Supervised Learning):<\/h3>\n<p>Denetimli \u00f6\u011frenme algoritmalar\u0131, etiketlenmi\u015f verilerle e\u011fitilir. Ba\u015fka bir deyi\u015fle, algoritma, her bir veri noktas\u0131n\u0131n do\u011fru sonucunu (\u00f6rne\u011fin, bir g\u00f6r\u00fcnt\u00fcn\u00fcn kedidir veya k\u00f6pe\u011fidir) \u00f6nceden bilir. Bu bilgiyi kullanarak, yeni, etiketlenmemi\u015f veriler i\u00e7in tahminler yapmay\u0131 \u00f6\u011frenir. Denetimli \u00f6\u011frenmenin iki yayg\u0131n alt kategorisi vard\u0131r: <\/p>\n<ul>\n<li><b>S\u0131n\u0131fland\u0131rma:<\/b> Verileri farkl\u0131 s\u0131n\u0131flara ay\u0131rmak i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, spam veya spam de\u011fil). Pop\u00fcler algoritmalar aras\u0131nda Lojistik Regresyon, Destek Vekt\u00f6r Makineleri (SVM), Karar A\u011fa\u00e7lar\u0131 ve Rastgele Ormanlar bulunur.<\/li>\n<li><b>Regresyon:<\/b> S\u00fcrekli de\u011ferleri tahmin etmek i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, bir evin fiyat\u0131). Yayg\u0131n \u00f6rnekler aras\u0131nda Do\u011frusal Regresyon, Polinom Regresyonu ve Destek Vekt\u00f6r Regresyonu yer al\u0131r.<\/li>\n<\/ul>\n<h3>2. Denetimsiz \u00d6\u011frenme (Unsupervised Learning):<\/h3>\n<p>Denetimsiz \u00f6\u011frenme algoritmalar\u0131, etiketlenmemi\u015f verilerle e\u011fitilir. Bu algoritmalar, verilerdeki kal\u0131plar\u0131, yap\u0131lar\u0131 veya ili\u015fkileri ke\u015ffetmeye \u00e7al\u0131\u015f\u0131r. Bu kategori, a\u015fa\u011f\u0131daki alt kategorileri i\u00e7erir:<\/p>\n<ul>\n<li><b>K\u00fcmeleme:<\/b> Verileri benzerliklerine g\u00f6re gruplara ay\u0131rmak i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, m\u00fc\u015fteri segmentasyonu). \u00d6nemli algoritmalar aras\u0131nda K-Ortalamalar\u0131, Hiyerar\u015fik K\u00fcmeleme ve DBSCAN bulunur.<\/li>\n<li><b>Boyut Azaltma:<\/b> Veri boyutunu azalt\u0131rken \u00f6nemli bilgileri korumak i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, veri g\u00f6rselle\u015ftirme). \u00d6rnekler aras\u0131nda Ba\u015f Bile\u015fen Analizi (PCA) ve t-SNE yer al\u0131r.<\/li>\n<li><b>Derin \u00d6\u011frenme (Deep Learning):<\/b> \u00c7ok katmanl\u0131 yapay sinir a\u011flar\u0131n\u0131 kullanarak verilerdeki karma\u015f\u0131k kal\u0131plar\u0131 \u00f6\u011frenir.<\/li>\n<\/ul>\n<h3>3. Peki\u015ftirmeli \u00d6\u011frenme (Reinforcement Learning):<\/h3>\n<p>Peki\u015ftirmeli \u00f6\u011frenme algoritmalar\u0131, bir ortamla etkile\u015fim kurarak \u00f6\u011frenirler. Algoritma, eylemlerinin sonu\u00e7lar\u0131na ba\u011fl\u0131 olarak \u00f6d\u00fcller veya cezalar al\u0131r ve bu geri bildirime dayanarak davran\u0131\u015f\u0131n\u0131 iyile\u015ftirir. Bu \u00f6\u011frenme t\u00fcr\u00fc, oyun oynama, robot kontrol\u00fc ve di\u011fer bir\u00e7ok uygulamada kullan\u0131l\u0131r. \u00d6rne\u011fin, bir robotun bir labirentte gezinmesini \u00f6\u011frenmesi bu kategoriye girer.<\/p>\n<h2>Algoritma T\u00fcr\u00fcne G\u00f6re Kategorizasyon<\/h2>\n<p>\u00d6\u011frenme t\u00fcr\u00fcne ek olarak, algoritmalar ayr\u0131ca i\u015flevlerine ve \u00e7al\u0131\u015fma \u015fekillerine g\u00f6re de kategorize edilebilir. Bu, geni\u015f bir \u00e7e\u015fitlilik sunmaktad\u0131r. \u00d6rne\u011fin, a\u011fa\u00e7 tabanl\u0131 algoritmalar (Karar A\u011fa\u00e7lar\u0131, Rastgele Ormanlar), do\u011frusal algoritmalar (Do\u011frusal Regresyon, Lojistik Regresyon) ve sinir a\u011flar\u0131 gibi daha karma\u015f\u0131k modeller bulunmaktad\u0131r.<\/p>\n<p>Ayr\u0131ca, algoritmalar\u0131n verimlilik ve hesaplama maliyetleri de farkl\u0131l\u0131k g\u00f6sterir. Baz\u0131 algoritmalar daha h\u0131zl\u0131 ve daha basitken, di\u011ferleri daha karma\u015f\u0131k veriler i\u00e7in daha g\u00fc\u00e7l\u00fc tahminler \u00fcretebilir. Dolay\u0131s\u0131yla, do\u011fru algoritman\u0131n se\u00e7imi, veri k\u00fcmesinin \u00f6zellikleri ve uygulama gereksinimlerine ba\u011fl\u0131d\u0131r.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>Makine \u00f6\u011frenmesi algoritmalar\u0131 \u00e7ok \u00e7e\u015fitlidir ve bunlar\u0131n do\u011fru bir \u015fekilde kategorize edilmesi, se\u00e7imini kolayla\u015ft\u0131r\u0131r. Bu makalede ele ald\u0131\u011f\u0131m\u0131z kategoriler, makine \u00f6\u011frenmesindeki \u00e7e\u015fitli algoritmalar\u0131 anlaman\u0131n ve uygulaman\u0131n temelini olu\u015fturur. Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> adresini ziyaret edebilirsiniz. <a href=\"https:\/\/dev.to\/rohit_prasain\/categorization-of-machine-learning-algorithms-15gb\">Bu \u0130ngilizce kaynak<\/a> da daha detayl\u0131 bilgi sunmaktad\u0131r.<\/p>\n<p><img decoding=\"async\" src=\"makineogrenmesi.jpg\" alt=\"Makine \u00d6\u011frenmesi \u015eemas\u0131\" width=\"500\"><\/p>\n<p>#Etiketler: Makine \u00d6\u011frenmesi, Algoritmalar, Denetimli \u00d6\u011frenme, Denetimsiz \u00d6\u011frenme, Peki\u015ftirmeli \u00d6\u011frenme, S\u0131n\u0131fland\u0131rma, Regresyon, K\u00fcmeleme, Boyut Azaltma, Yapay Zeka, AI, ML<br \/>\n<\/body><br \/>\n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-4071","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. Bu makalede, makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131 farkl\u0131 kategorilere ay\u0131rmay\u0131 ve her kategorinin \u00f6zelliklerini ayr\u0131nt\u0131l\u0131 olarak ele almay\u0131 ama\u00e7l\u0131yoruz.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-kategorizasyonu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu\" \/>\n<meta property=\"og:description\" content=\"Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. 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Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. 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Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. Bu makalede, makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131 farkl\u0131 kategorilere ay\u0131rmay\u0131 ve her kategorinin \u00f6zelliklerini ayr\u0131nt\u0131l\u0131 olarak ele almay\u0131 ama\u00e7l\u0131yoruz.","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:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-kategorizasyonu\/","og_locale":"tr_TR","og_type":"article","og_title":"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu","og_description":"Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. 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Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. 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{"id":4071,"date":"2024-11-17T01:15:16","date_gmt":"2024-11-16T22:15:16","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-kategorizasyonu\/"},"modified":"2024-11-17T01:15:16","modified_gmt":"2024-11-16T22:15:16","slug":"makine-ogrenmesi-algoritmalarinin-kategorizasyonu","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-kategorizasyonu\/","title":{"rendered":"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu"},"content":{"rendered":"

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\n Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu<\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<p>Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu<\/p>\n<p>Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. Bu makalede, makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131 farkl\u0131 kategorilere ay\u0131rmay\u0131 ve her kategorinin \u00f6zelliklerini ayr\u0131nt\u0131l\u0131 olarak ele almay\u0131 ama\u00e7l\u0131yoruz.<\/p>\n<h2>\u00d6\u011frenme T\u00fcr\u00fcne G\u00f6re Kategorizasyon<\/h2>\n<p>Makine \u00f6\u011frenmesi algoritmalar\u0131, \u00f6ncelikle nas\u0131l \u00f6\u011frendiklerine g\u00f6re s\u0131n\u0131fland\u0131r\u0131labilir. Bu a\u00e7\u0131dan \u00fc\u00e7 ana kategori vard\u0131r: <\/p>\n<h3>1. Denetimli \u00d6\u011frenme (Supervised Learning):<\/h3>\n<p>Denetimli \u00f6\u011frenme algoritmalar\u0131, etiketlenmi\u015f verilerle e\u011fitilir. Ba\u015fka bir deyi\u015fle, algoritma, her bir veri noktas\u0131n\u0131n do\u011fru sonucunu (\u00f6rne\u011fin, bir g\u00f6r\u00fcnt\u00fcn\u00fcn kedidir veya k\u00f6pe\u011fidir) \u00f6nceden bilir. Bu bilgiyi kullanarak, yeni, etiketlenmemi\u015f veriler i\u00e7in tahminler yapmay\u0131 \u00f6\u011frenir. Denetimli \u00f6\u011frenmenin iki yayg\u0131n alt kategorisi vard\u0131r: <\/p>\n<ul>\n<li><b>S\u0131n\u0131fland\u0131rma:<\/b> Verileri farkl\u0131 s\u0131n\u0131flara ay\u0131rmak i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, spam veya spam de\u011fil). Pop\u00fcler algoritmalar aras\u0131nda Lojistik Regresyon, Destek Vekt\u00f6r Makineleri (SVM), Karar A\u011fa\u00e7lar\u0131 ve Rastgele Ormanlar bulunur.<\/li>\n<li><b>Regresyon:<\/b> S\u00fcrekli de\u011ferleri tahmin etmek i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, bir evin fiyat\u0131). Yayg\u0131n \u00f6rnekler aras\u0131nda Do\u011frusal Regresyon, Polinom Regresyonu ve Destek Vekt\u00f6r Regresyonu yer al\u0131r.<\/li>\n<\/ul>\n<h3>2. Denetimsiz \u00d6\u011frenme (Unsupervised Learning):<\/h3>\n<p>Denetimsiz \u00f6\u011frenme algoritmalar\u0131, etiketlenmemi\u015f verilerle e\u011fitilir. Bu algoritmalar, verilerdeki kal\u0131plar\u0131, yap\u0131lar\u0131 veya ili\u015fkileri ke\u015ffetmeye \u00e7al\u0131\u015f\u0131r. Bu kategori, a\u015fa\u011f\u0131daki alt kategorileri i\u00e7erir:<\/p>\n<ul>\n<li><b>K\u00fcmeleme:<\/b> Verileri benzerliklerine g\u00f6re gruplara ay\u0131rmak i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, m\u00fc\u015fteri segmentasyonu). \u00d6nemli algoritmalar aras\u0131nda K-Ortalamalar\u0131, Hiyerar\u015fik K\u00fcmeleme ve DBSCAN bulunur.<\/li>\n<li><b>Boyut Azaltma:<\/b> Veri boyutunu azalt\u0131rken \u00f6nemli bilgileri korumak i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, veri g\u00f6rselle\u015ftirme). \u00d6rnekler aras\u0131nda Ba\u015f Bile\u015fen Analizi (PCA) ve t-SNE yer al\u0131r.<\/li>\n<li><b>Derin \u00d6\u011frenme (Deep Learning):<\/b> \u00c7ok katmanl\u0131 yapay sinir a\u011flar\u0131n\u0131 kullanarak verilerdeki karma\u015f\u0131k kal\u0131plar\u0131 \u00f6\u011frenir.<\/li>\n<\/ul>\n<h3>3. Peki\u015ftirmeli \u00d6\u011frenme (Reinforcement Learning):<\/h3>\n<p>Peki\u015ftirmeli \u00f6\u011frenme algoritmalar\u0131, bir ortamla etkile\u015fim kurarak \u00f6\u011frenirler. Algoritma, eylemlerinin sonu\u00e7lar\u0131na ba\u011fl\u0131 olarak \u00f6d\u00fcller veya cezalar al\u0131r ve bu geri bildirime dayanarak davran\u0131\u015f\u0131n\u0131 iyile\u015ftirir. Bu \u00f6\u011frenme t\u00fcr\u00fc, oyun oynama, robot kontrol\u00fc ve di\u011fer bir\u00e7ok uygulamada kullan\u0131l\u0131r. \u00d6rne\u011fin, bir robotun bir labirentte gezinmesini \u00f6\u011frenmesi bu kategoriye girer.<\/p>\n<h2>Algoritma T\u00fcr\u00fcne G\u00f6re Kategorizasyon<\/h2>\n<p>\u00d6\u011frenme t\u00fcr\u00fcne ek olarak, algoritmalar ayr\u0131ca i\u015flevlerine ve \u00e7al\u0131\u015fma \u015fekillerine g\u00f6re de kategorize edilebilir. Bu, geni\u015f bir \u00e7e\u015fitlilik sunmaktad\u0131r. \u00d6rne\u011fin, a\u011fa\u00e7 tabanl\u0131 algoritmalar (Karar A\u011fa\u00e7lar\u0131, Rastgele Ormanlar), do\u011frusal algoritmalar (Do\u011frusal Regresyon, Lojistik Regresyon) ve sinir a\u011flar\u0131 gibi daha karma\u015f\u0131k modeller bulunmaktad\u0131r.<\/p>\n<p>Ayr\u0131ca, algoritmalar\u0131n verimlilik ve hesaplama maliyetleri de farkl\u0131l\u0131k g\u00f6sterir. Baz\u0131 algoritmalar daha h\u0131zl\u0131 ve daha basitken, di\u011ferleri daha karma\u015f\u0131k veriler i\u00e7in daha g\u00fc\u00e7l\u00fc tahminler \u00fcretebilir. Dolay\u0131s\u0131yla, do\u011fru algoritman\u0131n se\u00e7imi, veri k\u00fcmesinin \u00f6zellikleri ve uygulama gereksinimlerine ba\u011fl\u0131d\u0131r.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>Makine \u00f6\u011frenmesi algoritmalar\u0131 \u00e7ok \u00e7e\u015fitlidir ve bunlar\u0131n do\u011fru bir \u015fekilde kategorize edilmesi, se\u00e7imini kolayla\u015ft\u0131r\u0131r. Bu makalede ele ald\u0131\u011f\u0131m\u0131z kategoriler, makine \u00f6\u011frenmesindeki \u00e7e\u015fitli algoritmalar\u0131 anlaman\u0131n ve uygulaman\u0131n temelini olu\u015fturur. Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> adresini ziyaret edebilirsiniz. <a href=\"https:\/\/dev.to\/rohit_prasain\/categorization-of-machine-learning-algorithms-15gb\">Bu \u0130ngilizce kaynak<\/a> da daha detayl\u0131 bilgi sunmaktad\u0131r.<\/p>\n<p><img decoding=\"async\" src=\"makineogrenmesi.jpg\" alt=\"Makine \u00d6\u011frenmesi \u015eemas\u0131\" width=\"500\"><\/p>\n<p>#Etiketler: Makine \u00d6\u011frenmesi, Algoritmalar, Denetimli \u00d6\u011frenme, Denetimsiz \u00d6\u011frenme, Peki\u015ftirmeli \u00d6\u011frenme, S\u0131n\u0131fland\u0131rma, Regresyon, K\u00fcmeleme, Boyut Azaltma, Yapay Zeka, AI, ML<br \/>\n<\/body><br \/>\n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-4071","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. Bu geli\u015fme, \u00e7e\u015fitli algoritmalar\u0131n kullan\u0131m\u0131yla ger\u00e7ekle\u015fir. Ancak, bu algoritmalar\u0131n say\u0131s\u0131 ve \u00e7e\u015fitlili\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, onlar\u0131 anlamak ve uygulamak zor olabilir. Bu makalede, makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131 farkl\u0131 kategorilere ay\u0131rmay\u0131 ve her kategorinin \u00f6zelliklerini ayr\u0131nt\u0131l\u0131 olarak ele almay\u0131 ama\u00e7l\u0131yoruz.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-kategorizasyonu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Kategorizasyonu\" \/>\n<meta property=\"og:description\" content=\"Makine \u00f6\u011frenmesi (ML), bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan veri \u00f6\u011frenip geli\u015ftirmelerini sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. 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