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Item #:
81840220
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
87% of respondents would recommend this to a friend
Item #:
81840220
AED 242
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This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
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What Stands Out
Comprehensive Content
Covers a wide range of machine learning concepts, from fundamentals to advanced techniques, ensuring readers gain a thorough understanding and practical skills to build intelligent systems.
Hands-On Approach
Emphasizes practical learning through projects and real-world examples, allowing readers to apply theories and tools like Scikit-Learn, Keras, and TensorFlow effectively.
Updated Edition
The third edition includes the latest advancements in machine learning, ensuring users are equipped with current knowledge and tools to tackle modern challenges in the field.
Product Details
- Book designed for readers with Python programming experience and familiarity with main scientific libraries
- Suitable for those interested in machine learning applications such as customer segmentation, product recommendation, fraud detection, and revenue forecasting
- Intended to provide concepts, tools, and intuition needed to implement programs capable of learning from data
- Covers a wide range of techniques, from simple to deep learning algorithms, utilizing production-ready Python frameworks like Scikit-Learn, TensorFlow, and Keras
- Scikit-Learn is user-friendly and implements many machine learning algorithms efficiently, making it a great entry point to learning machine learning
- TensorFlow and Keras offer more complex and high-level deep learning capabilities, with TensorFlow supporting large-scale machine learning applications
| Publisher | O'Reilly Media |
| Publication date | November 8, 2022 |
| Edition | 3rd |
| Language | English |
| Print length | 861 pages |
| ISBN-10 | 1098125975 |
| ISBN-13 | 978-1098125974 |
| Dimensions | 7.25 x 2 x 9.5 inches (18.4 x 5.1 x 24.1 cm) |
| Package Weight | 3.3 Pound |
Who Should Buy?
-
Aspiring Data Scientists
Designed for individuals eager to learn practical machine learning skills through step-by-step projects and example code.
-
AI Enthusiasts
Ideal for hobbyists or professionals looking to enhance their knowledge in artificial intelligence and deep learning frameworks.
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Developers Transitioning
Perfect for software developers wanting to transition into data science with hands-on, applied learning in Python.
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Absolute Beginners
Not suitable for those with no programming experience or foundational knowledge in machine learning concepts.
Product Description
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Python Editorial Review
The "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd Edition" Kindle edition has garnered mostly positive reviews. Customers have expressed that it is an excellent book for those studying machine learning and data science. It is particularly commended for its comprehensive coverage and clear illustrations of relevant topics. However, some reviewers voiced concerns about the quality of the physical book they received. The book has been recommended for both beginners and experts in the field. A customer with a solid background in Python programming expressed that it is necessary to fully comprehend the content. Despite the praise, there were mentions of the book's sentences being ambiguous and the style not being concise enough, prompting readers to seek external sources for clarity. **
Customer Reviews & Ratings
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Pros
- Comprehensive coverage of relevant topics
- Clear illustrations and in-depth explanations
- Suitable for both beginners and experts
Cons
- Some readers found the writing style ambiguous and not concise
Product Price History
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AED 242
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Features & Benefits
- Book explores machine learning techniques using Scikit-learn, Keras and TensorFlow
- Suitable for programmers with no prior experience in deep learning
- Covers a range of models from simple linear regression to deep neural networks
- Includes code examples and exercises throughout the book
- Dives into neural net architectures for computer vision, NLP, generative models, deep reinforcement learning
- Explores unsupervised learning techniques like clustering, dimensionality reduction and anomaly detection
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