Nº de artículo:
170012730
Machine Learning: A School of Artificial Intelligence with Its Theoretical Aspects and Python Applications
Nº de artículo:
170012730
PAB 20
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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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What Stands Out
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.
Detalles de producto
- Machine learning, expressed as a subfield of artificial intelligence, is widely used in many fields led by engineering, finance and bioinformatics. To develop machine learning applications, it is important to theoretically understand some algorithms that are based on calculus, linear algebra and statistics. After learning the theoretical aspects of these algorithms, the application can be developed by coding it with an easy and rich library language such as Python. The theoretical aspects of machine learning algorithms in the book have been meticulously examined, and the required linear algebra and statistics topics have also been briefly examined. Python applications have been developed for each algorithm using problems containing unique data sets. 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. After reading this book, deep learning architectures will be easier to understand.Who is this book for?• Those who want to start developing a Machine Learning app but don't know exactly where to start• Those who are currently developing machine learning applications• Those who prepare theses including Machine Learning and conduct scientific studies in the fields of Science, Engineering and Social SciencesPython and Required Installations Use of NumPy, Pandas and Matplotlib Libraries Types of Learning Application Development Processes in Machine Learning Data Pre-Processing Process with Python Linear Regression Polynomial Regression Multiple Linear Regression K-Nearest Neighbor Algorithm Naive Bayes Algorithm Logistics Regression Artificial Neural Networks Support Vector Machines Show more
| Publishers | Nobel Academic Publishing |
| Date of publication | March 4, 2020 |
| Prints | 3. |
| Language | Turkish |
| Print Length | 312 pages |
| ISBN-10 | 6050331766 |
| ISBN-13 | 978-6050331769 |
| Dimensions | 15 x 2 x 21.5 cm |
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Preguntas y respuestas de los clientes
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Pregunta:
Who is this book intended for?
Respuesta: It's designed for beginners starting in Machine Learning and those currently developing applications or preparing theses in related fields. -
Pregunta:
What programming language is primarily used in this book?
Respuesta: The book primarily uses Python, along with libraries such as NumPy, Pandas, and Matplotlib. -
Pregunta:
What topics are essential covered in this book?
Respuesta: 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. ###
Customer Reviews & Ratings
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ventajas
- Clear, straightforward writing style.
- Suitable for intermediate readers looking to advance in machine learning.
- Contains practical applications. ###
Contras
- May be too challenging for complete beginners.
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características y beneficios
- 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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