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Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
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Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.
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Produktdetails
| Publisher | Packt Publishing |
| Publication date | 31 July 2024 |
| Edition | 4th |
| Language | English |
| Print length | 518 pages |
| ISBN-10 | 1835085628 |
| ISBN-13 | 978-1835085622 |
| Item weight | 960 g |
| Dimensions | 19.05 x 3.02 x 23.5 cm |
Für wen ist das Produkt geeignet?
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Aspiring Data Scientists
Perfect for beginners looking to understand machine learning concepts through practical examples and real-world applications.
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Software Engineers
Ideal for developers wanting to incorporate machine learning into their software projects and enhance their technical skill set.
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Educators and Trainers
Great resource for instructors designing courses or workshops focused on practical applications of machine learning techniques.
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Advanced Practitioners
Not suitable for expert users seeking in-depth theoretical knowledge or advanced machine learning methodologies and algorithms.
PRODUKTBESCHREIBUNG
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
Kunden Fragen und Antworten
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Frage:
Is this book suitable for beginners?
Antworten: Yes, it's designed for both beginners and experienced practitioners. -
Frage:
What programming knowledge do I need?
Antworten: Basic Python programming knowledge is required. -
Frage:
Do I need additional software to follow along?
Antworten: You will need access to libraries such as PyTorch and TensorFlow for practical examples.
AI & Machine Learning Editorial Review
**** "Python Machine Learning By Example" authored by Yuxi (Hayden) Liu emerges as a premier resource for both budding enthusiasts and seasoned professionals in the field of machine learning (ML). The book's framework revolves around real-world applications, making it a practical guide rather than a theoretical treatise. With a hefty 500 pages, it dedicates a mere 34 pages to theory, placing the emphasis squarely on hands-on learning and application. This innovative approach aids readers in grasping the intricacies of ML concepts while simultaneously developing relevant skills. The book covers an extensive variety of modern and relevant topics, including stock price forecasting, image categorization, and online advertising. Liu's expertise—gleaned from his tenure at industry leader Google—imbues the text with depth and credibility. Each chapter provides clear explanations and practical coding examples that not only explain how algorithms work but also delve into the underlying 'why,' fostering a deeper understanding of the material. Readers have especially praised the clarity of the writing, as well as the structured manner in which complex subjects, such as deep learning and reinforcement learning, are presented. Liu's inclusion of cutting-edge techniques along with best practices provides insightful guidance for translating theoretical knowledge into real-world ML projects. The Consensus among customers is that this book delivers immense value for learners at any stage of their journey—beginners benefit from the foundational insights, while experienced data scientists can refine and deepen their skills. Overall, "Python Machine Learning By Example" is heralded as a treasure trove of knowledge, crafted in a way that bridges theory and practice, engaging readers in their pursuit of machine learning mastery. **
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Vorteile
- Strong emphasis on real-world applications.
- Clear and concise explanations of ML algorithms.
- Practical coding examples enhance comprehension.
- Coverage of advanced techniques like deep learning and reinforcement learning.
- Written by an experienced industry expert.
Nachteile
- Limited theoretical discussion may not satisfy those looking for exhaustive theoretical context.
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Merkmale und Vorteile
- Comprehensive guide for all levels in machine learning.
- Emphasizes machine learning best practices throughout.
- Includes hands-on examples using PyTorch, TensorFlow, and scikit-learn.
- Covers advanced techniques like NLP transformers and multimodal models.
- Provides insights from an experienced Google ML engineer.
- Free PDF copy included with print or Kindle purchase.
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