Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 2nd edition
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Product Details
| Country of Origin | United States |
| Personalized | No |
| Features | Revised Edition |
| ISBN | 9781492032649 |
| Subject Area | Mathematics, Computers |
| Publication Name | Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems |
| Publisher | O'reilly Media, Incorporated |
| Item Length | 9.4 in (23.88 cm) |
| Subject | Intelligence (Ai) & Semantics, General, Data Processing, Computer Vision & Pattern Recognition |
| Publication Year | 2019 |
| Type | Textbook |
| Format | Trade Paperback |
| Language | Eng,Swa |
| Item Height | 1.4 in (3.56 cm) |
| Author | Auru00e9lien Gu00e9ron |
| Item Weight | 43.2 Oz (1224.72 grams) |
| Item Width | 7 in (17.78 cm) |
| Number of Pages | 848 Pages |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for individuals looking to gain practical machine learning skills through hands-on projects and real-world examples.
-
Machine Learning Practitioners
Great for professionals wanting to deepen their understanding of Scikit-Learn, Keras, and TensorFlow for advanced applications.
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Students and Educators
A valuable resource for students studying data science or educators teaching machine learning concepts in academic settings.
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Complete Beginners
Not suitable for those without a foundational understanding of programming or statistics; prior knowledge is required.
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Editorial Review
Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow 2nd Edition is an essential textbook for anyone interested in building intelligent systems using contemporary machine learning techniques. This revised edition offers a comprehensive overview of the concepts, tools, and techniques necessary for effective data processing and computer vision applications. With 848 pages of insightful content written by Aurélien Géron, it serves as a valuable resource for both beginners and experienced practitioners. The book covers a variety of subject areas including AI and data processing, making it a well-rounded choice for those studying mathematics or computer science.
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Pros
- Comprehensive coverage of machine learning concepts
- Written by recognized expert Aurélien Géron
- Revised edition with updated techniques
- Suitable for both beginners and professionals
- Includes practical examples and applications
Cons
- Some readers may find it quite lengthy
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