Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017
Start your journey into machine learning with step-by-step instructions from an expert on the classic scikit-learn library.
Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017
Articol nr.: 87921355

Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017

Articol nr.: 87921355

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Shop Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017 online at a best price in Romania. 4873117984
Publisher u30aau30e9u30a4u30eau30fcu30b8u30e3u30d1u30f3
Publication date May 25, 2017
Edition First Edition
Language Japanese
Print length 373 pages
ISBN-10 4873117984
ISBN-13 978-4873117980
Item Weight 680 g
Dimensions 9.45 x 7.48 x 0.98 inches (24 x 19 x 2.5 cm)

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Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017

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Întrebări și răspunsuri ale clienților

  • întrebare: Who is the author of this book?

    Răspuns: The author is a seasoned expert and release manager for scikit-learn.
  • întrebare: What topics are covered in this book?

    Răspuns: The book covers machine learning basics, feature engineering, and model evaluation.
  • întrebare: Is this book suitable for beginners?

    Răspuns: Yes, it provides a solid foundation for individuals starting their machine learning journey.

Andreas C. Muller , Sarah Guido , 中田秀基 & 0 Electricity & Communications Editorial Review

The book, "Start Machine Learning with Python," has received positive reception from readers, particularly those who are new to machine learning and want to learn through practical examples using the scikit-learn library. Customers appreciate the way the author explains complex topics, particularly unsupervised learning and feature engineering, without heavy reliance on mathematical formulas. The book appears to be accessible yet comprehensive, covering key topics such as supervised and unsupervised learning, model evaluation, and the usage of Python code examples, which many found helpful in their learning process. Readers have noted that the practical approaches and sample codes provided throughout the chapters significantly enhance the learning experience. The chapter on model evaluation and improvement has been highlighted as a key strength, with many expressing that the techniques discussed are invaluable for anyone facing challenges in evaluating models. Additionally, the explanations of the scikit-learn pipeline feature are praised for their usefulness. However, some users point out areas of improvement. Certain readers found the sections on unsupervised learning and text data handling a bit challenging, particularly if they did not have prior knowledge of these subjects. There were also comments regarding the reliance on the author's custom library, "mglearn," which some found to be too opaque, making it difficult to understand the examples fully. Additionally, the presence of the matplotlib library in sample code without sufficient background explanations left some readers confused. Overall, "Start Machine Learning with Python" is Considered a strong resource for those looking to grasp the fundamentals of machine learning, especially if they already possess some basic understanding of the subject. It is best suited for individuals who are eager to dive into practical applications with scikit-learn rather than complete beginners in programming or machine learning. **

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Pro

  • Clear explanations of complex topics, especially in unsupervised learning.
  • Practical examples and Python code using scikit-learn.
  • Strong focus on model evaluation and improvement.
  • Useful information on scikit-learn's pipeline feature.

Contra

  • Some chapters may be challenging for absolute beginners without prior knowledge.

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