{"id":477912,"date":"2023-08-09T09:22:19","date_gmt":"2023-08-09T09:22:19","guid":{"rendered":""},"modified":"2023-11-30T04:27:38","modified_gmt":"2023-11-30T04:27:38","slug":"machine-learning-ml","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/machine-learning-ml\/","title":{"rendered":"Makine \u00d6\u011frenimi (ML)"},"content":{"rendered":"<p>\u200bMakine \u00d6\u011frenimi (ML), verilerden \u00f6\u011frenen ve verilere \u00f6zerk bir \u015fekilde uyum sa\u011flayan sistemler olu\u015fturmaya odaklanan bir yapay zeka (AI) alt k\u00fcmesidir. Bilgisayarlar\u0131n deneyimlerden \u00f6\u011frenmesine ve a\u00e7\u0131k programlamaya gerek kalmadan kararlar almas\u0131na olanak sa\u011flayan bir teknolojidir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Makine \u00d6\u011freniminin Evrimi<\/h2>\n\n\n\n<p>Makine \u00d6\u011frenimi kavram\u0131n\u0131n k\u00f6keni 20. y\u00fczy\u0131l\u0131n ortalar\u0131na kadar uzanabilir. Bilgisayar alan\u0131nda \u00f6nc\u00fc olan Alan Turing, &quot;Makineler d\u00fc\u015f\u00fcnebilir mi?&quot; sorusunu sordu. 1950&#039;de bir makinenin ak\u0131ll\u0131 davran\u0131\u015f sergileme yetene\u011fini belirlemek i\u00e7in Turing Testi&#039;nin geli\u015ftirilmesine yol a\u00e7t\u0131. Resmi &quot;Makine \u00d6\u011frenimi&quot; terimi, 1959&#039;da Amerikal\u0131 IBM&#039;ci ve bilgisayar oyunlar\u0131 ve yapay zeka alan\u0131nda \u00f6nc\u00fc olan Arthur Samuel taraf\u0131ndan icat edildi.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"1024\" src=\"https:\/\/oneproxy.pro\/wp-content\/uploads\/2023\/11\/Machine_Learning.png\" alt=\"Makine \u00f6\u011frenme\" class=\"wp-image-497656\" title=\"\" srcset=\"https:\/\/oneproxy.pro\/wp-content\/uploads\/2023\/11\/Machine_Learning.png 1024w, https:\/\/oneproxy.pro\/wp-content\/uploads\/2023\/11\/Machine_Learning-150x150.png 150w, https:\/\/oneproxy.pro\/wp-content\/uploads\/2023\/11\/Machine_Learning-768x768.png 768w, https:\/\/oneproxy.pro\/wp-content\/uploads\/2023\/11\/Machine_Learning-12x12.png 12w, https:\/\/oneproxy.pro\/wp-content\/uploads\/2023\/11\/Machine_Learning-75x75.png 75w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Makine \u00d6\u011freniminin Temel \u00d6zellikleri<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Algoritmalar<\/strong>: ML algoritmalar\u0131, verilerdeki kal\u0131plar\u0131 tan\u0131mlamak gibi bir sorunu \u00e7\u00f6zmeye veya bir g\u00f6revi ger\u00e7ekle\u015ftirmeye y\u00f6nelik talimatlard\u0131r.<\/li>\n\n\n\n<li><strong>Model E\u011fitimi<\/strong>: Bir algoritman\u0131n \u00f6\u011frenmesine, tahminlerde bulunmas\u0131na veya kararlar almas\u0131na yard\u0131mc\u0131 olmak i\u00e7in veriyi bir algoritmaya beslemeyi i\u00e7erir.<\/li>\n\n\n\n<li><strong>Denetimli \u00d6\u011frenme<\/strong>: Model, etiketli e\u011fitim verilerinden \u00f6\u011frenir, sonu\u00e7lar\u0131n tahmin edilmesine veya verilerin s\u0131n\u0131fland\u0131r\u0131lmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n\n\n\n<li><strong>Denetimsiz \u00d6\u011frenme<\/strong>: Model, genellikle etiketlenmemi\u015f verilerle ilgilenerek bilgiyi ke\u015ffetmek i\u00e7in kendi ba\u015f\u0131na \u00e7al\u0131\u015f\u0131r.<\/li>\n\n\n\n<li><strong>Takviyeli \u00d6\u011frenme<\/strong>: Model, kendi eylemlerinden ve deneyimlerinden elde edilen geri bildirimleri kullanarak deneme yan\u0131lma yoluyla \u00f6\u011frenir.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Uygulamalar ve Zorluklar<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Uygulamalar<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Tahmine Dayal\u0131 Analitik: Finans, pazarlama ve operasyonlarda kullan\u0131l\u0131r.<\/li>\n\n\n\n<li>G\u00f6r\u00fcnt\u00fc ve Konu\u015fma Tan\u0131ma: G\u00fcvenlik ve dijital asistanlardaki uygulamalara g\u00fc\u00e7 verir.<\/li>\n\n\n\n<li>\u00d6neri Sistemleri: E-ticaret ve ak\u0131\u015f hizmetleri taraf\u0131ndan kullan\u0131l\u0131r.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Zorluklar<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Veri Gizlili\u011fi: ML modellerinde kullan\u0131lan hassas bilgilerin gizlili\u011finin sa\u011flanmas\u0131.<\/li>\n\n\n\n<li>\u00d6nyarg\u0131 ve Adillik: Adil algoritmalar sa\u011flamak i\u00e7in e\u011fitim verilerindeki \u00f6nyarg\u0131lar\u0131n \u00fcstesinden gelmek.<\/li>\n\n\n\n<li>Hesaplama Gereksinimleri: B\u00fcy\u00fck veri k\u00fcmelerinin i\u015flenmesi i\u00e7in y\u00fcksek hesaplama g\u00fcc\u00fc gerekir.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Kar\u015f\u0131la\u015ft\u0131rmal\u0131 analiz<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>\u00d6zellik<\/th><th>Makine \u00f6\u011frenme<\/th><th>Geleneksel Programlama<\/th><\/tr><\/thead><tbody><tr><td>Yakla\u015fmak<\/td><td>Veriye dayal\u0131 karar verme<\/td><td>Kurala dayal\u0131 karar verme<\/td><\/tr><tr><td>Esneklik<\/td><td>Yeni verilere uyum sa\u011flar<\/td><td>Statik, manuel g\u00fcncelleme gerektirir<\/td><\/tr><tr><td>Karma\u015f\u0131kl\u0131k<\/td><td>Karma\u015f\u0131k sorunlar\u0131 \u00e7\u00f6zebilir<\/td><td>\u00d6nceden tan\u0131mlanm\u0131\u015f senaryolarla s\u0131n\u0131rl\u0131d\u0131r<\/td><\/tr><tr><td>\u00d6\u011frenme<\/td><td>Devaml\u0131 geli\u015fme<\/td><td>\u00d6\u011frenme yetene\u011fi yok<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Gelecek Beklentileri ve Teknolojiler<\/h2>\n\n\n\n<p>Makine \u00d6\u011freniminin gelece\u011fi a\u015fa\u011f\u0131daki alanlardaki geli\u015fmelerle i\u00e7 i\u00e7edir:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Kuantum hesaplama<\/strong>: Makine \u00f6\u011frenimi modelleri i\u00e7in hesaplama g\u00fcc\u00fcn\u00fcn art\u0131r\u0131lmas\u0131.<\/li>\n\n\n\n<li><strong>Sinir A\u011f\u0131 Mimarileri<\/strong>: Daha karma\u015f\u0131k ve verimli modellerin geli\u015ftirilmesi.<\/li>\n\n\n\n<li><strong>A\u00e7\u0131klanabilir Yapay Zeka (XAI)<\/strong>: Makine \u00f6\u011frenimi kararlar\u0131n\u0131n daha \u015feffaf ve anla\u015f\u0131l\u0131r hale getirilmesi.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Proxy Sunucularla Entegrasyon<\/h2>\n\n\n\n<p>Proxy sunucular\u0131 Makine \u00d6\u011freniminde \u00e7e\u015fitli \u015fekillerde \u00f6nemli bir rol oynayabilir:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Veri toplama<\/strong>: Anonimli\u011fi ve g\u00fcvenli\u011fi korurken \u00e7e\u015fitli k\u00fcresel kaynaklardan b\u00fcy\u00fck veri k\u00fcmelerinin toplanmas\u0131n\u0131 kolayla\u015ft\u0131r\u0131n.<\/li>\n\n\n\n<li><strong>Co\u011frafi test<\/strong>: G\u00fcvenilirlik ve do\u011fruluklar\u0131ndan emin olmak i\u00e7in ML modellerini farkl\u0131 co\u011frafi konumlarda test edin.<\/li>\n\n\n\n<li><strong>Y\u00fck dengeleme<\/strong>: Verimli ML i\u015fleme i\u00e7in hesaplama y\u00fcklerini farkl\u0131 sunuculara da\u011f\u0131t\u0131n.<\/li>\n\n\n\n<li><strong>G\u00fcvenlik<\/strong>: ML sistemlerini siber tehditlere ve yetkisiz eri\u015fime kar\u015f\u0131 koruyun.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">\u0130lgili Ba\u011flant\u0131lar<\/h2>\n\n\n\n<p>Makine \u00d6\u011frenimi hakk\u0131nda daha fazla bilgi i\u00e7in \u015fu kaynaklar\u0131 g\u00f6z \u00f6n\u00fcnde bulundurun:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Machine_learning\" rel=\"nofollow noopener\" target=\"_blank\">Makine \u00d6\u011frenimi - Vikipedi<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/ai.googleblog.com\/\" rel=\"nofollow noopener\" target=\"_blank\">Google Yapay Zeka Blogu<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/ocw.mit.edu\/courses\/electrical-engineering-and-computer-science\/6-036-introduction-to-machine-learning-fall-2020\/index.htm\" rel=\"nofollow noopener\" target=\"_blank\">MIT Makine \u00d6\u011frenimi Kursu<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.coursera.org\/specializations\/deep-learning\" rel=\"nofollow noopener\" target=\"_blank\">Coursera&#039;da Andrew Ng&#039;den Derin \u00d6\u011frenme Uzmanl\u0131\u011f\u0131<\/a><\/li>\n<\/ol>\n\n\n\n<p>Bu makale, Makine \u00d6\u011freniminin, tarihsel ge\u00e7mi\u015finin, temel \u00f6zelliklerinin, uygulamalar\u0131n\u0131n, zorluklar\u0131n\u0131n ve gelecekteki y\u00f6nelimlerinin yan\u0131 s\u0131ra proxy sunucu teknolojileriyle potansiyel entegrasyonunun kapsaml\u0131 bir \u015fekilde anla\u015f\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>","protected":false},"featured_media":468824,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477912","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark><\/mark>","faq_items":[{"question":"What is Machine Learning (ML) and how is it different from Artificial Intelligence (AI)?","answer":"Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on algorithms and statistical models enabling computers to learn from patterns and make decisions. While ML is about learning from data and making predictions or decisions, AI encompasses a broader field that includes ML, emphasizing intelligent behavior in machines."},{"question":"What are the key historical milestones in the development of Machine Learning?","answer":"The history of Machine Learning includes the Bayes' theorem in the 18th century, the coining of the term \"machine learning\" by Arthur Samuel in 1959, early work on the Perceptron model in the 1950s, the development of decision trees in the 1960s, Support Vector Machines in the 1990s, and the rise of Deep Learning in the 2000s."},{"question":"How does the internal structure of Machine Learning work?","answer":"The internal structure of Machine Learning consists of the input layer, hidden layers, output layer, weights, biases, loss function, and optimization algorithm. Data is fed into the model through the input layer, processed in hidden layers using mathematical functions, and then the output layer produces the final prediction. Weights and biases are adjusted during training to minimize error, guided by the loss function and optimization algorithm."},{"question":"What are the main types of Machine Learning (ML)?","answer":"The main types of Machine Learning are Supervised Learning (trained on labeled data to make predictions), Unsupervised Learning (learning from unlabeled data to find hidden patterns), and Reinforcement Learning (learning through trial and error, receiving rewards or penalties for actions)."},{"question":"What are some common applications and problems of Machine Learning (ML), and how are they addressed?","answer":"Common applications of Machine Learning include healthcare, finance, transportation, and entertainment. Problems include bias and fairness, data privacy, and computational costs. These can be addressed through ethical guidelines, encryption, and the development of efficient algorithms."},{"question":"How do proxy servers like OneProxy relate to Machine Learning (ML)?","answer":"Proxy servers like OneProxy are used in Machine Learning for data collection, privacy protection, load balancing, and geo-targeting. They facilitate access to global data for training, mask IP addresses during sensitive research, distribute computational loads, and enable location-specific analyses."},{"question":"What are some emerging trends and future perspectives related to Machine Learning (ML)?","answer":"Emerging trends in Machine Learning include Quantum Computing, Explainable AI, Personalized Medicine, and Sustainability. These innovations leverage quantum mechanics, provide understandable insights, tailor healthcare to individual needs, and utilize ML for environmental protection."}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477912","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477912\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468824"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=477912"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}