{"id":477910,"date":"2023-08-09T09:22:19","date_gmt":"2023-08-09T09:22:19","guid":{"rendered":""},"modified":"2023-09-05T11:15:41","modified_gmt":"2023-09-05T11:15:41","slug":"machine-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/machine-learning\/","title":{"rendered":"Makine \u00f6\u011frenme"},"content":{"rendered":"<p>Makine \u00f6\u011frenimi (ML), sistemlere a\u00e7\u0131k\u00e7a programlanmadan otomatik olarak \u00f6\u011frenme ve deneyimlerden geli\u015fme yetene\u011fi sa\u011flayan bir yapay zeka (AI) dal\u0131d\u0131r. Bu \u00f6\u011frenme s\u00fcreci, verilerdeki karma\u015f\u0131k kal\u0131plar\u0131n tan\u0131nmas\u0131na ve bunlara dayanarak ak\u0131ll\u0131 kararlar al\u0131nmas\u0131na dayanmaktad\u0131r.<\/p>\n<h2>Makine \u00d6\u011freniminin K\u00f6keninin Tarihi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Makine \u00f6\u011frenimi kavram olarak 20. y\u00fczy\u0131l\u0131n ba\u015flar\u0131na kadar uzan\u0131yor ancak k\u00f6kleri daha da ilerilere uzanabiliyor. Verilerden \u00f6\u011frenebilen makineler yapma fikri 1950&#039;lerde \u015fekillenmeye ba\u015flad\u0131.<\/p>\n<ul>\n<li><strong>1950:<\/strong> Alan Turing, bir makinenin ak\u0131ll\u0131 davran\u0131\u015f sergileyip sergileyemeyece\u011fini belirlemek i\u00e7in bir y\u00f6ntem \u00f6neren Turing Testini ba\u015flatt\u0131.<\/li>\n<li><strong>1957:<\/strong> Frank Rosenblatt, ilk yapay sinir a\u011flar\u0131ndan biri olan Perceptron&#039;u tasarlad\u0131.<\/li>\n<li><strong>1960&#039;lar ve 1970&#039;ler:<\/strong> Karar a\u011fa\u00e7lar\u0131 ve destek vekt\u00f6r makineleri gibi algoritmalar\u0131n geli\u015ftirilmesi.<\/li>\n<li><strong>1980&#039;ler:<\/strong> Ba\u011flant\u0131c\u0131 devrim sinir a\u011flar\u0131n\u0131n yeniden canlanmas\u0131na yol a\u00e7t\u0131.<\/li>\n<li><strong>1990&#039;lar:<\/strong> Daha karma\u015f\u0131k algoritmalar\u0131n ortaya \u00e7\u0131k\u0131\u015f\u0131, geli\u015fmi\u015f hesaplama g\u00fcc\u00fc ve b\u00fcy\u00fck veriler, makine \u00f6\u011freniminin b\u00fcy\u00fcmesini h\u0131zland\u0131rd\u0131.<\/li>\n<\/ul>\n<h2>Makine \u00d6\u011frenimi Hakk\u0131nda Detayl\u0131 Bilgi: Makine \u00d6\u011frenimi Konusunu Geni\u015fletmek<\/h2>\n<p>Makine \u00f6\u011frenimi, girdi verilerini alabilen ve bir \u00e7\u0131kt\u0131y\u0131 tahmin etmek i\u00e7in istatistiksel teknikleri kullanabilen algoritmalar olu\u015fturmay\u0131 i\u00e7erir. Ba\u015fl\u0131ca \u00f6\u011frenme t\u00fcrleri \u015funlard\u0131r:<\/p>\n<ol>\n<li><strong>Denetimli \u00d6\u011frenme:<\/strong> Model etiketli veriler \u00fczerinde e\u011fitilir.<\/li>\n<li><strong>Denetimsiz \u00d6\u011frenme:<\/strong> Model etiketlenmemi\u015f veriler \u00fczerinde e\u011fitilir.<\/li>\n<li><strong>Takviyeli \u00d6\u011frenme:<\/strong> Model, bir \u00e7evreyle etkile\u015fime girerek ve \u00f6d\u00fcller veya cezalar alarak \u00f6\u011frenir.<\/li>\n<\/ol>\n<h3>Uygulamalar<\/h3>\n<ul>\n<li>Tahmine dayal\u0131 analitik<\/li>\n<li>Konu\u015fma tan\u0131ma<\/li>\n<li>G\u00f6r\u00fcnt\u00fc i\u015fleme<\/li>\n<li>Do\u011fal dil i\u015fleme<\/li>\n<\/ul>\n<h2>Makine \u00d6\u011freniminin \u0130\u00e7 Yap\u0131s\u0131: Makine \u00d6\u011frenimi Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Makine \u00f6\u011frenimi modelleri genellikle belirli bir yap\u0131y\u0131 takip eder:<\/p>\n<ol>\n<li><strong>Veri toplama:<\/strong> Ham verilerin toplanmas\u0131.<\/li>\n<li><strong>Veri \u00d6n \u0130\u015fleme:<\/strong> Verilerin temizlenmesi ve kullan\u0131labilir bir formata d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi.<\/li>\n<li><strong>Model Se\u00e7imi:<\/strong> Do\u011fru algoritmay\u0131 se\u00e7mek.<\/li>\n<li><strong>Modeli E\u011fitmek:<\/strong> \u0130\u015flenen verinin algoritmaya beslenmesi.<\/li>\n<li><strong>De\u011ferlendirme:<\/strong> Modelin do\u011frulu\u011funun test edilmesi.<\/li>\n<li><strong>Da\u011f\u0131t\u0131m:<\/strong> Modelin ger\u00e7ek d\u00fcnyadaki bir uygulamaya uygulanmas\u0131.<\/li>\n<li><strong>\u0130zleme ve G\u00fcncelleme:<\/strong> Modelin d\u00fczenli bak\u0131m\u0131 ve g\u00fcncellenmesi.<\/li>\n<\/ol>\n<h2>Makine \u00d6\u011freniminin Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>Makine \u00f6\u011freniminin baz\u0131 temel \u00f6zellikleri \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>Uyarlanabilirlik:<\/strong> Yeni verileri veya de\u011fi\u015fen ortamlar\u0131 \u00f6\u011frenebilir ve bunlara uyum sa\u011flayabilir.<\/li>\n<li><strong>Tahmin Do\u011frulu\u011fu:<\/strong> Verilere dayal\u0131 olarak do\u011fru tahminler veya kararlar verebilme yetene\u011fi.<\/li>\n<li><strong>Otomasyon:<\/strong> \u0130nsan m\u00fcdahalesi olmadan g\u00f6revleri yerine getirme yetene\u011fi.<\/li>\n<li><strong>Karma\u015f\u0131kl\u0131k:<\/strong> Geni\u015f ve karma\u015f\u0131k veri k\u00fcmelerini y\u00f6netme.<\/li>\n<\/ul>\n<h2>Makine \u00d6\u011frenimi T\u00fcrleri: Yap\u0131land\u0131r\u0131lm\u0131\u015f Bir Genel Bak\u0131\u015f<\/h2>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Tan\u0131m<\/th>\n<th>\u00d6rnekler<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Denetimli \u00d6\u011frenme<\/td>\n<td>Etiketli verilerden \u00f6\u011frenme<\/td>\n<td>Regresyon, S\u0131n\u0131fland\u0131rma<\/td>\n<\/tr>\n<tr>\n<td>Denetimsiz \u00d6\u011frenme<\/td>\n<td>Etiketlenmemi\u015f verilerden \u00f6\u011frenme<\/td>\n<td>K\u00fcmelenme, Dernek<\/td>\n<\/tr>\n<tr>\n<td>Takviyeli \u00d6\u011frenme<\/td>\n<td>Deneme yan\u0131lma yoluyla \u00f6\u011frenme<\/td>\n<td>Oyun Oynama, Robotik<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Makine \u00d6\u011frenimini Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<h3>Kullan\u0131m Yollar\u0131<\/h3>\n<ul>\n<li>Sa\u011fl\u0131k te\u015fhisi<\/li>\n<li>Finansal tahmin<\/li>\n<li>Otonom ara\u00e7lar<\/li>\n<li>Doland\u0131r\u0131c\u0131l\u0131k tespiti<\/li>\n<\/ul>\n<h3>Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h3>\n<ul>\n<li><strong>A\u015f\u0131r\u0131 uyum g\u00f6sterme:<\/strong> Bir model e\u011fitim verilerinde iyi performans g\u00f6sterdi\u011finde, ancak g\u00f6r\u00fcnmeyen verilerde k\u00f6t\u00fc performans g\u00f6sterdi\u011finde.\n<ul>\n<li><em>\u00c7\u00f6z\u00fcm:<\/em> \u00c7apraz do\u011frulama, D\u00fczenlile\u015ftirme.<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u00d6n yarg\u0131:<\/strong> Bir modelin girdi verileri hakk\u0131nda hatalara yol a\u00e7an varsay\u0131mlarda bulunmas\u0131.\n<ul>\n<li><em>\u00c7\u00f6z\u00fcm:<\/em> \u00c7e\u015fitli veri k\u00fcmelerinden yararlan\u0131n.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>Terim<\/th>\n<th>\u00d6zellikler<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Makine \u00f6\u011frenme<\/td>\n<td>Otomatik \u00f6\u011frenme, model e\u011fitimi, tahmine dayal\u0131 analiz<\/td>\n<\/tr>\n<tr>\n<td>Yapay zeka<\/td>\n<td>Ak\u0131l y\u00fcr\u00fctme ve problem \u00e7\u00f6zmeyi de i\u00e7eren daha geni\u015f bir kavram olan ML&#039;yi kapsar<\/td>\n<\/tr>\n<tr>\n<td>Veri madencili\u011fi<\/td>\n<td>ML&#039;ye benzer ancak b\u00fcy\u00fck veri k\u00fcmelerindeki kal\u0131plar\u0131 ke\u015ffetmeye odaklan\u0131r<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Makine \u00d6\u011frenimine \u0130li\u015fkin Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<ul>\n<li><strong>Kuantum hesaplama:<\/strong> Hesaplama g\u00fcc\u00fcn\u00fcn art\u0131r\u0131lmas\u0131.<\/li>\n<li><strong>A\u00e7\u0131klanabilir Yapay Zeka:<\/strong> Karma\u015f\u0131k modelleri daha anla\u015f\u0131l\u0131r hale getirmek.<\/li>\n<li><strong>U\u00e7 Bilgi \u0130\u015flem:<\/strong> Veriler olu\u015fturulduklar\u0131 yere daha yak\u0131n i\u015fleniyor.<\/li>\n<li><strong>Nesnelerin \u0130nterneti ile entegrasyon:<\/strong> Geli\u015fmi\u015f otomasyon ve ger\u00e7ek zamanl\u0131 karar verme.<\/li>\n<\/ul>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Makine \u00d6\u011frenimiyle Nas\u0131l \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy gibi proxy sunucular a\u015fa\u011f\u0131dakileri sa\u011flayarak makine \u00f6\u011freniminde tamamlay\u0131c\u0131 bir rol oynayabilir:<\/p>\n<ul>\n<li><strong>Veri Anonimle\u015ftirme:<\/strong> Veri toplarken gizlili\u011fin korunmas\u0131.<\/li>\n<li><strong>Veri toplama:<\/strong> \u00c7e\u015fitli kaynaklardan verimli bir \u015fekilde veri toplamak.<\/li>\n<li><strong>Y\u00fck dengeleme:<\/strong> Hesaplamal\u0131 i\u015f y\u00fcklerini da\u011f\u0131tarak daha h\u0131zl\u0131 e\u011fitim ve tahmin yap\u0131lmas\u0131n\u0131 kolayla\u015ft\u0131r\u0131r.<\/li>\n<li><strong>G\u00fcvenlik:<\/strong> Verilerin ve modellerin b\u00fct\u00fcnl\u00fc\u011f\u00fcn\u00fc korumak.<\/li>\n<\/ul>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/see.stanford.edu\/Course\/CS229\" target=\"_new\" rel=\"noopener nofollow\">Stanford&#039;da Makine \u00d6\u011frenimi<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/\" target=\"_new\" rel=\"noopener nofollow\">Scikit-Learn: Python&#039;da Makine \u00d6\u011frenimi<\/a><\/li>\n<li><a href=\"https:\/\/www.tensorflow.org\/\" target=\"_new\" rel=\"noopener nofollow\">TensorFlow: U\u00e7tan Uca A\u00e7\u0131k Kaynakl\u0131 Makine \u00d6\u011frenimi Platformu<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy: G\u00fcvenli Proxy Sunucular\u0131<\/a><\/li>\n<\/ul>\n<p>Okuyucular, makine \u00f6\u011freniminin k\u00f6kenlerini, temel \u00f6zelliklerini, uygulamalar\u0131n\u0131 ve gelecek perspektiflerini anlayarak bu d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fc teknolojiye dair fikir sahibi olurlar. OneProxy gibi proxy sunucularla olan ili\u015fki, modern makine \u00f6\u011freniminin \u00e7ok y\u00f6nl\u00fc ve dinamik do\u011fas\u0131n\u0131 daha da vurgulamaktad\u0131r.<\/p>","protected":false},"featured_media":477911,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477910","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Machine Learning: An In-Depth Guide<\/mark>","faq_items":[{"question":"What is Machine Learning and How Does It Work?","answer":"<p>Machine learning is a branch of artificial intelligence that enables systems to learn from data and make decisions without explicit programming. It involves collecting and preprocessing data, selecting a suitable algorithm, training the model on this data, evaluating its accuracy, deploying it in real-world applications, and ongoing monitoring and updating.<\/p>"},{"question":"What Are the Key Features of Machine Learning?","answer":"<p>The key features of machine learning include adaptability to new data, predictive accuracy, automation, and the ability to manage complex data sets. These features enable machine learning to provide intelligent, data-driven decisions across various applications.<\/p>"},{"question":"What Are the Different Types of Machine Learning?","answer":"<p>There are three main types of machine learning: Supervised Learning, where the model learns from labeled data; Unsupervised Learning, where the model learns from unlabeled data; and Reinforcement Learning, where the model learns by interacting with an environment, receiving rewards or penalties.<\/p>"},{"question":"How Are Proxy Servers Like OneProxy Associated with Machine Learning?","answer":"<p>Proxy servers like OneProxy can be associated with machine learning by providing data anonymization, data aggregation, load balancing, and security. These features help in protecting privacy, gathering data efficiently, distributing computational workloads, and ensuring the integrity of data and models.<\/p>"},{"question":"What Are Some Common Problems in Machine Learning, and How Can They Be Solved?","answer":"<p>Common problems in machine learning include overfitting, where the model performs well on training data but poorly on unseen data, and bias, where the model makes assumptions leading to errors. Solutions include techniques like cross-validation and regularization for overfitting, and utilizing diverse data sets to minimize bias.<\/p>"},{"question":"What Are the Future Perspectives and Technologies Related to Machine Learning?","answer":"<p>Future perspectives in machine learning include quantum computing to enhance computational power, explainable AI to make models more understandable, edge computing for processing data closer to where it's generated, and integration with IoT for real-time decision-making and enhanced automation.<\/p>"},{"question":"How Can I Learn More About Machine Learning?","answer":"<p>You can learn more about machine learning by visiting resources like Stanford's Machine Learning course, Scikit-Learn for Python-based learning, TensorFlow for an open-source machine learning platform, or exploring proxy server solutions like OneProxy for specific data-related applications. Links to these resources are provided at the end of the article.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477910","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\/477910\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/477911"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=477910"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}