Artikelnr.:
170012730
Makine Öğrenmesi: Teorik Yönleri ve Pyhton Uygulamaları İle Bir Yapay Zeka Ekolü
Artikelnr.:
170012730
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Learning the basic information in this book will enable people who want to develop applications with Deep Learning, a subfield of machine learning, to create an important infrastructure.
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Wat opvalt
Comprehensive Insights
Offers extensive coverage of both theoretical aspects and practical applications of machine learning, ensuring readers grasp the full scope of the subject.
Python Focus
Integrates Python applications, providing readers with hands-on experience and real-world examples to enhance their understanding and skills in machine learning.
Educational Resource
Serves as an invaluable resource for students and educators alike, catering to both newcomers and individuals seeking to deepen their AI knowledge.
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- Yapay zekanın bir alt alanı olarak ifade edilen makine öğrenmesi mühendislik, finans ve biyoinformatik'in başı çektiği birçok alanda yaygın olarak kullanılmaktadır. Makine öğrenmesi uygulamaları geliştirmek için temelinde kalkülüs, doğrusal cebir ve istatistik barındıran bazı algoritmaların teorik olarak kavranması önemlidir. Bu algoritmaların teorik yönleri öğrenildikten sonra Python gibi kolay ve zengin kütüphane yapısına sahip bir programlama dili ile kodlanarak uygulama geliştirilebilir. Kitaptaki makine öğrenmesi algoritmalarının teorik yönleri titizlikle irdelenmiş, gerek duyulan doğrusal cebir ve istatistik konuları da özet olarak incelenmiştir. Özgün veri setleri içeren problemler kullanılarak her algoritma için Python uygulamaları geliştirilmiştir. Makine öğrenmesinin bir alt alanı olan Derin Öğrenme ile uygulama geliştirmek isteyen kişilerin de özellikle bu kitaptaki temel bilgileri öğrenmesi önemli bir alt yapı oluşturmalarını sağlayacaktır. Bu kitabı okuduktan sonra derin öğrenme mimarilerinin anlaşılması daha kolay olacaktır.Bu kitap kimler içindir?• Makine Öğrenmesi uygulaması geliştirmeye başlamak isteyen ancak nereden başlaması gerektiğini tam olarak bilmeyenler• Halihazırda Makine öğrenmesi uygulamaları geliştirenler• Fen, Mühendislik ve Sosyal Bilimler alanlarında Makine Öğrenmesi içeren tezler hazırlayanlar ve bilimsel çalışmalar yapanlarPython ve Gerekli Kurulumlar NumPy, Pandas ve Matplotlib Kütüphanelerinin Kullanımı Öğrenme Türleri Makine Öğrenmesinde Uygulama Geliştirme Süreçleri Python ile Veri Ön İşleme Süreci Doğrusal Regresyon Polinom Regresyon Çoklu Doğrusal Regresyon K-En Yakın Komşu Algoritması Naive Bayes Algoritması Lojistik Regresyon Yapay Sinir Ağları Destek Vektör Makinaları Daha fazla göster
| Publishers | Nobel Academic Publishing |
| Date of publication | March 4, 2020 |
| Prints | 3. |
| Taal | Turks |
| Afdruklengte | 312 pages |
| ISBN-10 | 6050331766 |
| ISBN-13 | 978-6050331769 |
| Afmetingen | 15 x 2 x 21.5 cm |
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Who is this book intended for?
antwoord: It's designed for beginners starting in Machine Learning and those currently developing applications or preparing theses in related fields. -
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What programming language is primarily used in this book?
antwoord: The book primarily uses Python, along with libraries such as NumPy, Pandas, and Matplotlib. -
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What topics are essential covered in this book?
antwoord: It covers theoretical aspects of algorithms, data processing, and several application development processes in Machine Learning.
Sinan Uğuz Format: Kağıt Kapak Editorial Review
The book "Machine Learning: A School of Artificial Intelligence with Its Theoretical Aspects and Python Applications" has garnered a mixed reception among readers, particularly regarding its suitability for different experience levels in machine learning. Users have appreciated the author's clear and straightforward writing style, making it accessible to those with intermediate knowledge of the subject. However, several reviewers pointed out that the content may be overwhelming for complete beginners, emphasizing that a basic understanding of the topic is essential to fully benefit from the material. While the book offers valuable resources for advancing in the field, some users noted that it occasionally includes basic topics that they found easily available online. These sections detracted from the overall depth of the book, suggesting that the text could be more focused on providing advanced insights rather than covering elementary concepts. Overall, the book is recommended for those looking to enhance their existing knowledge of machine learning but may be of limited use for readers starting from scratch. ###
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- Clear, straightforward writing style.
- Suitable for intermediate readers looking to advance in machine learning.
- Contains practical applications. ###
Nadelen
- May be too challenging for complete beginners.
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Kenmerken en voordelen
- Comprehensive guide to Machine Learning and Deep Learning.
- Covers theoretical aspects vital for understanding algorithms.
- Practical applications developed using Python's rich libraries.
- Perfect for both beginners and those currently developing applications.
- Includes topics from linear algebra and statistics for foundational knowledge.
- Enhances the ability to understand deep learning architectures.
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