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Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
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Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence.
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Detalji o proizvodu
| Publisher | O'Reilly Media |
| Publication date | April 16, 2019 |
| Edition | 1st |
| Language | English |
| Print length | 362 pages |
| ISBN-10 | 1492035645 |
| ISBN-13 | 978-1492035640 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 0.75 x 9 inches (17.8 x 1.9 x 22.9 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to deepen their understanding of unsupervised learning techniques in Python.
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Machine Learning Enthusiasts
Great for enthusiasts aiming to apply unsupervised learning solutions to real-world problems without labeled datasets.
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Students and Educators
Useful for students and educators wishing to explore machine learning concepts with practical Python applications.
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Beginners in Programming
Not suitable for those with little to no programming knowledge, as it covers advanced topics extensively.
OPIS PROIZVODA
Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
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Intelligence & Semantics Editorial Review
This book has received mixed reception from customers. Some reviewers found the technical explanations to be poor and the code to be riddled with bugs. They also criticized the lack of depth in explaining concepts and the use of labeled datasets. However, there were positive reviews praising the author's writing style and approach in explaining the topic. Overall, this book seems to have disappointed readers looking for a comprehensive guide to unsupervised learning.
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Pros
- The author uses a methodical approach in explaining the topic
- Provides multiple ways to test a model
- Includes examples for better feature engineering
Protiv
- Poor technical explanations
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Značajke i prednosti
- Unsupervised learning is the next frontier in AI and may hold the key to AGI
- Majority of the world's data is unlabeled, making it unsuitable for conventional supervised learning
- Unsupervised learning can discover meaningful patterns in unlabeled datasets
- The book provides hands-on experience and code examples using Python frameworks
- Readers can apply unsupervised learning to uncover hidden patterns, obtain business insight, detect anomalies, and more
- Prerequisites include some Python programming experience and familiarity with relevant mathematical concepts
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