Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017
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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
Numéro d'article: 87921355

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

Numéro d'article: 87921355

MUR 1732

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Ce qui se démarque

Beginner-Friendly Approach
This book offers a clear and concise introduction for beginners, making complex concepts in machine learning accessible and easy to understand with practical examples using Python and scikit-learn.
Focus on Feature Engineering
Emphasizing feature engineering, the book provides essential techniques and strategies, equipping readers with skills to enhance model performance and tackle real-world data challenges effectively.
Hands-On Learning
With practical exercises and real-world projects, readers can apply their knowledge immediately, reinforcing learning and boosting confidence in applying machine learning techniques in various scenarios.

Détails du produit

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 Mauritius. 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)

À qui est-ce destiné ?

Suitable For
  • Beginner Programmers

    Ideal for those new to programming who want to grasp machine learning fundamentals using Python.

  • Data Enthusiasts

    Perfect for individuals interested in exploring data science and machine learning applications through hands-on experience.

  • Self-learners

    Great for independent learners seeking structured material for understanding feature engineering and scikit-learn.

Not Suitable For
  • Advanced Users

    Not suitable for experienced practitioners already familiar with machine learning concepts and scikit-learn.

  • Academic Researchers

    May not meet the advanced theoretical knowledge demands typical of academic research in machine learning.

  • Busy Professionals

    Not ideal for individuals with limited time who require concise, high-level machine learning over detailed tutorials.

DESCRIPTION DU PRODUIT

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

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: Who is the author of this book?

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

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

    répondre: 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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Avantages

  • 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.

Les inconvénients

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

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Caractéristiques et avantages

  • 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.