Nº de artículo:
87943583
3rd Edition Python Machine Learning Programming Theory and Practice by Master Data Scientists impress top gear Tankobon Softcover – October 22, 2020
82% of respondents would recommend this to a friend
Nº de artículo:
87943583
NIO 1550
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The definitive commentary bridging theory and practice!
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What Stands Out
Comprehensive Coverage
Covers both theoretical concepts and practical applications of Python in machine learning, ensuring readers grasp essential techniques and can apply them in real-world scenarios.
Expert Insights
Authored by Master Data Scientists, providing invaluable guidance and insider tips that enhance understanding and improve coding practices for aspiring machine learning professionals.
Updated Content
Released as the 3rd edition, it includes the latest advancements and tools in Python machine learning, equipping users with current skills essential for today's data-driven industry.
Detalles de producto
| Publisher | u30a4u30f3u30d7u30ecu30b9 (October 22, 2020) |
| Publication date | October 22, 2020 |
| Language | Japanese |
| Tankobon Softcover | 688 pages |
| ISBN-10 | 4295010073 |
| ISBN-13 | 978-4295010074 |
| Dimensions | 7.28 x 1.14 x 9.21 inches (18.5 x 2.9 x 23.4 cm) |
Who Should Buy?
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Beginner Programmers
Ideal for those new to Python and machine learning concepts, providing clear explanations and practical examples.
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Data Science Students
Perfect for academic use, offering a blend of theory and practice suited for data science courses.
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Professionals Upskilling
Beneficial for industry professionals seeking to enhance their skills in Python and machine learning applications.
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Experienced Developers
Advanced users may find the content basic, lacking the depth needed for seasoned programmers.
DESCRIPCIÓN DEL PRODUCTO
3rd Edition Python Machine Learning Programming Theory and Practice by Master Data Scientists impress top gear Tankobon Softcover – October 22, 2020
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Sebastian Raschka , Vahid Mirjalili Editorial Review
**** The product in question, a book on Python and machine learning, has garnered mixed yet mostly positive feedback from its users. Readers predominantly highlight that the book is tailored for those with a moderate level of Python knowledge, suggesting it's not beginner-friendly. Several reviewers recommend that aspiring learners of machine learning first engage with more introductory materials. Once past the initial learning curve, users find this book to be an excellent resource combining theory and practical implementation of machine learning concepts using Python. Reviewers appreciate the clarity in the explanations and the effective coding examples that come with the text. Many emphasize that the book serves as a solid reference for intermediate to advanced learners looking to deepen their understanding of machine learning in conjunction with Python programming. The content is described as engaging and comprehensive enough to provide a full overview of machine learning concepts from a Python perspective, making it worthwhile for any serious student or practitioner in the field. However, it's worth noting that some users expressed disappointment over the lack of features like a searchable text option in the Kindle version, which they found could enhance the learning experience. Despite this minor drawback, readers feel they have made a wise investment in their education and express enthusiasm about carrying their machine learning studies forward using the methodologies and insights gained from this book. **Pros and Cons:** **
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ventajas
- Suitable for intermediate to advanced learners.
- Effective explanations and practical coding examples.
- Comprehensive coverage of machine learning concepts.
- Highly recommended for those seeking to deepen their understanding.
Contras
- Not beginner-friendly; prior Python knowledge recommended.
Product Price History
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características y beneficios
- Best-selling machine learning book, now in its 3rd edition.
- Covers comprehensive machine learning concepts, including deep learning and reinforcement learning.
- Compatible with cutting-edge Python libraries like scikit-learn and TensorFlow.
- Updated chapters with insights from experienced authors.
- Translated into multiple languages and internationally recognized.
- Ideal for readers wanting to bridge the gap between theory and practical application.
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