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Item #:
67893645
Adaptive Machine Learning Algorithms with Python: Solve Data Analytics and Machine Learning Problems on Edge Devices
88% of respondents would recommend this to a friend
Item #:
67893645
GHS 791
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What Stands Out
Edge Computing Focus
Utilizes adaptive algorithms specifically designed for edge devices, enabling efficient data processing and real-time analytics where conventional methods fall short.
Practical Implementation
Offers hands-on guidance through Python examples, ensuring users can apply complex machine learning concepts directly to real-world data problems.
Versatile Solutions
Addresses a broad range of data analytics challenges, making it suitable for data scientists, engineers, and developers looking to enhance their machine learning skill set.
Product Details
| Publisher | Apress |
| Publication date | March 13, 2022 |
| Edition | First Edition |
| Language | English |
| Print length | 297 pages |
| ISBN-10 | 1484280164 |
| ISBN-13 | 978-1484280164 |
| Item Weight | 1 pounds (450 grams) |
| Dimensions | 6.1 x 0.68 x 9.25 inches (15.5 x 1.7 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists seeking to implement machine learning solutions on edge devices using Python.
-
Developers
Great for developers who need to integrate adaptive machine learning algorithms into software applications effectively.
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Tech Enthusiasts
Perfect for tech enthusiasts wanting to deepen their understanding of machine learning applications on edge computing.
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Beginners in Python
Not suitable for those new to Python or programming, as a foundational knowledge is essential for understanding.
Product Description
Adaptive Machine Learning Algorithms with Python: Solve Data Analytics and Machine Learning Problems on Edge Devices
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Intelligence & Semantics Editorial Review
This book is a comprehensive guide on using adaptive machine learning algorithms with Python to solve data analytics and machine learning problems on edge devices. It provides a step-by-step approach to understanding and implementing adaptive machine learning algorithms, with a focus on practical applications. The book covers a wide range of topics, including data preprocessing, feature selection, model training, and evaluation. The author provides clear explanations, code examples, and real-world case studies to help readers learn and apply the algorithms effectively. Overall, this book is a valuable resource for anyone interested in leveraging the power of adaptive machine learning algorithms on edge devices.
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Pros
- Comprehensive guide on using adaptive machine learning algorithms with Python
- Includes step-by-step approach and practical applications
- Clear explanations and code examples provided
- Real-world case studies included
Cons
- May not be suitable for beginners with no prior knowledge of machine learning
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