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Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017
90% of respondents would recommend this to a friend
HKD 278
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Start your journey into machine learning with step-by-step instructions from an expert on the classic scikit-learn library.
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產品詳情
| 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) |
Who Should Buy?
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Beginner Programmers
Ideal for those new to programming who want to grasp machine learning fundamentals using Python.
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Data Enthusiasts
Perfect for individuals interested in exploring data science and machine learning applications through hands-on experience.
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Self-learners
Great for independent learners seeking structured material for understanding feature engineering and scikit-learn.
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Advanced Users
Not suitable for experienced practitioners already familiar with machine learning concepts and scikit-learn.
產品敘述
Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017
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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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優點
- 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.
缺點
- Some chapters may be challenging for absolute beginners without prior knowledge.
Product Price History
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HKD 278
現在訂購,約可在下列時間收貨: 星期五, 十月 09
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特色和優勢
- Expert guidance from a seasoned scikit-learn release manager.
- Step-by-step instructions for mastering machine learning basics.
- In-depth coverage of feature engineering and model evaluation.
- Build highly accurate predictive models with practical techniques.
- Unique features not found in conventional machine learning manuals.
- Perfect for beginners looking to establish a solid foundation.
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