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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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Produkta informācija
| Publisher | Packt Publishing |
| Publication date | July 31, 2024 |
| Edition | 4th |
| Language | English |
| Print length | 518 pages |
| ISBN-10 | 1835085628 |
| ISBN-13 | 978-1835085622 |
| Item Weight | 2.12 pounds (960 grams) |
| Dimensions | 7.5 x 1.19 x 9.25 inches (19.1 x 3 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners looking to build practical machine learning skills through real-world examples and easy-to-follow instructions.
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Practical Learners
Great for those who prefer hands-on learning to grasp complex concepts through practical applications and case studies.
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Professionals Seeking Skills
Beneficial for working professionals who want to enhance their machine learning knowledge with applicable examples for the industry.
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Total Newcomers
Not suitable for absolute beginners with no programming knowledge or prior understanding of machine learning concepts.
PRODUKTA APRAKSTS
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
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Machine Theory Editorial Review
**** "Python Machine Learning by Example" emerges as a standout resource for both newcomers and those looking to solidify their understanding of machine learning concepts through practical implementations. The fourth edition of this book is particularly welcoming for individuals who might be daunted by complex algorithms, as it excels in simplifying intricate topics with clear explanations paired with real-world use cases. Readers have noted the book's strong emphasis on best practices in machine learning, which is indispensable for aspiring data scientists, machine learning engineers, and analysts. The author not only teaches the "how" behind programming algorithms but delves into the "why," fostering a deeper comprehension of machine learning principles. Moreover, the inclusion of hands-on exercises utilizing popular frameworks such as PyTorch and TensorFlow enables readers to build their competencies through direct application of learned concepts. The book effectively covers a broad range of critical topics, from classification and regression techniques to advanced subjects like deep learning, natural language processing, and reinforcement learning. In doing so, it caters to a wide spectrum of interests and skill levels, ensuring it's relevant for entry-level enthusiasts as well as more experienced practitioners seeking to revisit foundational knowledge. Reviewers particularly appreciated the extensive array of practical examples, such as stock price predictions and image classification with convolutional neural networks (CNNs), which not only engage the reader but also solidify the book's teachings. The structured approach to presenting complex materials makes this book a valuable asset in the toolkit of anyone eager to navigate the evolving landscape of machine learning. Overall, "Python Machine Learning by Example" receives high praise for its accessibility, thorough content, and practical orientation, making it an essential read for those entering or looking to advance in the machine learning field. **
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Pros
- Comprehensive coverage of machine learning concepts with real-world examples
- Clear explanations that simplify complex algorithms
- Emphasis on best practices and hands-on exercises
- Relevant for both beginners and experienced practitioners
- Incorporates advanced topics like deep learning and NLP
- Accessible coding exercises using popular frameworks (e.g., TensorFlow, PyTorch)
Mīnusi
- Some readers find the book outdated for the latest advancements in the field
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Svarīga informācija
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Funkcijas un priekšrocības
- 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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