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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
87% of respondents would recommend this to a friend
KRW 63550
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Product Details
| Publisher | O'Reilly Media |
| Publication date | May 9, 2017 |
| Edition | 1st |
| Language | English |
| Print length | 572 pages |
| ISBN-10 | 1491962291 |
| ISBN-13 | 978-1491962299 |
| Item Weight | 2.12 pounds (960 grams) |
| Dimensions | 7 x 1.29 x 9.19 inches (17.8 x 3.3 x 23.3 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for individuals starting their careers in data science, providing practical tools and essential concepts for machine learning.
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Software Engineers
Software professionals looking to expand their skills in AI and machine learning will find valuable insights and applications.
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Students in AI
University students studying artificial intelligence or related fields can benefit from hands-on examples and comprehensive techniques.
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Beginners in Coding
Individuals with no programming experience may find the content too advanced and challenging to comprehend effectively.
Product Description
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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Intelligence & Semantics Editorial Review
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems provides an excellent introduction to machine learning, balancing practical implementation with theoretical concepts. Readers appreciate the engaging writing style and practical examples, making complex topics approachable. The accompanying Jupyter Notebooks are particularly valuable, as they contain code implementations and additional resources for better understanding. Many users found the book helpful for self-study and noted its clarity in explaining foundational concepts related to Scikit-Learn and TensorFlow. Overall, this book serves beginners and intermediate learners well, equipping them with the tools needed to build intelligent systems effectively.
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Pros
- Practical focus on examples over complex mathematics
- Engaging writing style enhances learning experience
- Useful Jupyter Notebooks available for practical coding
- Up-to-date with recent techniques in machine learning
- Covers both Scikit-Learn and TensorFlow effectively
Cons
- Some details in the code may require GitHub supplement
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