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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
XOF 44793
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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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Customer Questions & Answers
-
Question:
What topics are covered in 'Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow'?
Answer: This book provides an extensive overview of essential machine learning concepts, algorithms, and tools. It covers Scikit-Learn for traditional ML techniques, Keras for building neural networks, and TensorFlow for deep learning and complex model implementations. Readers will explore practical applications, such as image classification, natural language processing, and regression analysis, equipping them with the knowledge to build robust and intelligent systems. -
Question:
Who is this book intended for?
Answer: The book is designed for aspiring data scientists, software engineers, and machine learning enthusiasts. Whether you’re a beginner seeking foundational knowledge or an intermediate learner wanting to deepen your understanding of deep learning frameworks, this resource is suitable for you. Practical examples and hands-on projects cater to various skill levels, making it a valuable asset for anyone wanting to excel in machine learning. -
Question:
What programming language is primarily used in this book?
Answer: This book predominantly uses Python, which is the most popular programming language in machine learning and data science. Python’s simplicity and readability make it an ideal choice for educators and practitioners alike. The book demonstrates how to implement machine learning techniques using Python libraries, allowing readers to quickly learn to use these powerful tools in their projects. -
Question:
Are there practical projects included in the book?
Answer: Yes, the book features several practical projects that guide you through real-world applications of machine learning. These projects include hands-on coding exercises where you can apply what you've learned, such as building models for image recognition and predictive analysis. Engaging in these projects helps reinforce theoretical knowledge while providing an opportunity to solve actual problems using machine learning. -
Question:
How does this book differ from other machine learning resources?
Answer: This book stands out due to its focus on practical implementation alongside theoretical knowledge. While many resources only cover concepts, this book encourages readers to engage in hands-on exercises using popular frameworks. Its structured approach combines theory, coding, and real-world scenarios, providing a well-rounded learning experience that equips you with both knowledge and practical skills. -
Question:
Is prior knowledge of machine learning required to read this book?
Answer: No prior knowledge is strictly required to read this book. It begins with the basics of machine learning and gradually progresses to more advanced topics. However, familiarity with Python programming is beneficial, as it allows readers to follow along with code examples and exercises effectively. The book is designed to make machine learning accessible to beginners while still being informative for those with some background knowledge. -
Question:
Can this book help me prepare for a career in data science?
Answer: Absolutely! This book serves as a comprehensive resource that equips you with the necessary skills for a career in data science. By covering essential machine learning techniques and providing hands-on experience, it prepares you for tasks commonly faced in data science roles, such as model development, data analysis, and algorithm implementation. Completing the projects will bolster your portfolio, showcasing your practical skills to potential employers. -
Question:
What kind of challenges or exercises does the book provide?
Answer: The book includes numerous challenges and exercises at the end of each chapter, designed to reinforce key concepts. These challenges vary in difficulty and encourage creative problem-solving using the tools and techniques covered in the book. Completing these exercises not only solidifies your understanding but also prepares you to tackle real-world data science problems effectively. -
Question:
Is there a companion website or additional resources available for this book?
Answer: Yes, the author often provides supplementary materials on a companion website or a dedicated GitHub repository. These resources may include code examples, datasets, and updates to keep the content current. Access to additional resources enhances the learning experience, allowing readers to delve deeper into topics and have practical support as they work on their projects. -
Question:
Where can I buy Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow in Senegal?
Answer: You can purchase 'Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow' at Ubuy, which offers a comprehensive shopping experience with a wide selection of books. Ubuy provides detailed product descriptions, user reviews, and various purchasing options tailored to customers in Senegal. By shopping at Ubuy, you can ensure that you receive your desired book efficiently and conveniently.
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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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