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With the increasing data volume today, various methods and techniques are needed to make data-based decisions. Machine learning, which is a subset of artificial intelligence and has been frequently used for this purpose in recent years, has achieved great success in a wide variety of fields, ranging from education to production, and health to logistics. The ability to learn is the most basic feature of intelligence, and the purpose of machine learning is to try to develop machines that have the same intelligence level as humans. Machine learning is not only a technology but also a field that brings together many disciplines, embraces a wide audience, including students, researchers, and professionals, and has been developing rapidly and carrying us into the future. Another main focus is to investigate the accuracy with which programmed computers and machines produce results. Thus, the theoretical knowledge within this field and the principles of the algorithms to be used in the systems have gradually advanced. The best way to obtain information about a subject is to look at it from all perspectives. For this reason, it is thought to be very important to understand and learn the theory, approaches, problems, algorithms, and assumptions of machine learning. This book aims to provide a comprehensive guide to readers who want to gain knowledge and application skills in machine learning, especially on how to work with supervised and unsupervised learning algorithms using the powerful capabilities of the Microsoft Azure Machine Learning Studio (classic) Platform. Microsoft Azure Machine Learning Studio (classic) platform helps users in the process of obtaining information from data by providing many conveniences to users in developing cloud-based machine learning applications and distributing them as a web service. This book will guide researchers working on machine learning in fields such as computer science, engineering, statistics, and social sciences. It will also be a guide for engineers who will develop a certain application with basic methods. The book, which consists of ten chapters including theoretical and empirical studies, aims to eliminate the lack of resources in the Turkish literature about the Microsoft Azure Machine Learning Studio (classic) platform.

Bilgisayar sistemlerinin insan benzeri zeka ve yetenekler kazanmasını amaçlayan yapay zeka uygulamaları günümüzde hemen hemen her alanda farklı kullanım şekilleri ile karşımıza çıkmaktadır. Yapay zeka çalışmalarının alt alanlarından birisi olan makine öğrenmesi ise geleneksel programlamaya ihtiyaç duymadan verileri temel alan, belirli görevlere ilişkin performansları geliştirebilen ve yeni veriler üzerinde karar vermek için geçmiş deneyimlerden veya örneklerden öğrenebilen sistem veya modeller oluşturulmasına odaklanmaktadır. Bu nedenle makine öğrenmesi uygulamaları geleneksel programlamadaki kurallar kullanarak verilerden istenen çıktıların elde edilmesinden farklılık göstermektedir. Bu kitap, makine öğrenmesi konusunda bilgi ve uygulama becerisi kazanmak isteyen okuyuculara, özellikle de Microsoft Azure Machine Learning Studio (classic) Platformunun güçlü yeteneklerini kullanarak denetimli ve denetimsiz öğrenme algoritmalarıyla nasıl çalışabileceklerine dair kapsamlı bir rehber sunmayı amaçlamaktadır. Microsoft Azure Machine Learning Studio (classic) platformu, makine öğrenmesi uygulamalarını bulut tabanlı olarak geliştirilmesi ve web servisi olarak dağıtılması konusunda kullanıcılara birçok kolaylık sağlayarak verilerden bilgi elde edilmesi sürecinde yardımcı olmaktadır. Teorik ve ampirik çalışmaları içeren on bölümünden oluşan kitap ile Microsoft Azure Machine Learning Studio (classic) platformu hakkında Türkçe literatürde bulunan kaynak eksikliğinin giderilmesi hedeflenmektedir.

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