Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python
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Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python
Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python
แƒ”แƒ แƒ—แƒ”แƒฃแƒšแƒ˜แƒก #: 38597813

Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python

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แƒ”แƒ แƒ—แƒ”แƒฃแƒšแƒ˜แƒก #: 38597813

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Shop Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python online at a best price in Georgia. 1484253728
  • Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine.All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complexmachine learning algorithms.Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python.What You'll LearnWork with simple and complex datasets common to Scikit-LearnManipulate data into vectors and matrices for algorithmic processingBecome familiar with the Anaconda distribution used in data scienceApply machine learning with Classifiers, Regressors, and Dimensionality ReductionTune algorithms and find the best algorithms for each datasetLoad data from and save to CSV, JSON, Numpy, and Pandas formatsWho This Book Is ForThe aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.
Publisher Apress
Publication date November 18, 2019
Edition First Edition
Language English
Print length 255 pages
ISBN-10 1484253728
ISBN-13 978-1484253724
Item Weight 1 pounds (450 grams)
Dimensions 7.01 x 0.58 x 10 inches (17.8 x 1.5 x 25.4 cm)

แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒแƒฆแƒฌแƒ”แƒ แƒ˜แƒšแƒแƒ‘แƒ

Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python

แƒ’แƒแƒฅแƒ•แƒ— แƒ แƒแƒ˜แƒ›แƒ” แƒจแƒ”แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ? แƒ›แƒแƒ’แƒ•แƒ›แƒแƒ แƒ—แƒ”แƒ— แƒฉแƒแƒ—แƒจแƒ˜

แƒ›แƒแƒ›แƒฎแƒ›แƒแƒ แƒ”แƒ‘แƒ”แƒšแƒ—แƒ แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ”แƒ‘แƒ˜ แƒ“แƒ แƒžแƒแƒกแƒฃแƒฎแƒ”แƒ‘แƒ˜

  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: แƒ แƒแƒ’แƒแƒ  แƒ•แƒ˜แƒงแƒ˜แƒ“แƒแƒ— Hands-on Scikit-Learn for Machine Learning แƒแƒœแƒšแƒแƒ˜แƒœ Ubuy-แƒ“แƒแƒœ?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ›แƒแƒ แƒขแƒ˜แƒ•แƒ˜แƒ แƒงแƒ˜แƒ“แƒ•แƒ Hands-on Scikit-Learn for Machine Learning แƒแƒœแƒšแƒแƒ˜แƒœ Ubuy-แƒ“แƒแƒœ.. แƒ—แƒฅแƒ•แƒ”แƒœ แƒฃแƒ‘แƒ แƒแƒšแƒแƒ“ แƒฃแƒœแƒ“แƒ แƒ›แƒแƒซแƒ”แƒ‘แƒœแƒแƒ— แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜, แƒแƒ˜แƒ แƒฉแƒ˜แƒแƒ— แƒ—แƒฅแƒ•แƒ”แƒœแƒ˜ แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒ—แƒแƒ“แƒ˜ แƒจแƒ”แƒ›แƒแƒฌแƒ›แƒ”แƒ‘แƒ˜แƒก แƒ“แƒ แƒแƒก แƒ“แƒ แƒ›แƒ˜แƒ˜แƒขแƒแƒœแƒแƒ— แƒ˜แƒ’แƒ˜ แƒ—แƒฅแƒ•แƒ”แƒœแƒก แƒแƒ“แƒ’แƒ˜แƒšแƒแƒก.
  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: Hands-on Scikit-Learn for Machine Learning แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜แƒ แƒแƒœแƒšแƒแƒ˜แƒœ แƒจแƒแƒžแƒ˜แƒœแƒ’แƒ˜แƒกแƒ—แƒ•แƒ˜แƒก Georgia-แƒจแƒ˜?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ“แƒ˜แƒแƒฎ, Ubuy Georgia-แƒจแƒ˜ แƒ”แƒก แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒ—แƒฅแƒ•แƒ”แƒœแƒ—แƒ•แƒ˜แƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜แƒ แƒ’แƒแƒœแƒ˜แƒ•แƒ แƒฃแƒš แƒคแƒแƒกแƒแƒ“.. Hands-on Scikit-Learn for Machine Learning แƒแƒ  แƒแƒ แƒ˜แƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜ แƒแƒ“แƒ’แƒ˜แƒšแƒแƒ‘แƒ แƒ˜แƒ•แƒแƒ“, แƒ›แƒแƒ’แƒ แƒแƒ› แƒจแƒ”แƒ’แƒ˜แƒซแƒšแƒ˜แƒแƒ— แƒ’แƒ•แƒฏแƒ”แƒ แƒแƒ“แƒ”แƒก แƒฉแƒ•แƒ”แƒœแƒ˜ แƒ”แƒฅแƒกแƒžแƒ แƒ”แƒก แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒกแƒ”แƒ แƒ•แƒ˜แƒกแƒ”แƒ‘แƒ˜.
  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: แƒ แƒแƒ›แƒ“แƒ”แƒœแƒ˜ แƒ“แƒ แƒ แƒกแƒญแƒ˜แƒ แƒ“แƒ”แƒ‘แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ›แƒ˜แƒฆแƒ”แƒ‘แƒแƒก แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒก แƒ’แƒแƒคแƒแƒ แƒ›แƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒ›แƒ“แƒ”แƒ’?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ—แƒฅแƒ•แƒ”แƒœแƒ˜ แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒšแƒ˜ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ“แƒ แƒ แƒ’แƒแƒœแƒกแƒฎแƒ•แƒแƒ•แƒ“แƒ”แƒ‘แƒ แƒ—แƒฅแƒ•แƒ”แƒœ แƒ›แƒ˜แƒ”แƒ  แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒšแƒ˜ แƒ“แƒ แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒ—แƒแƒ“แƒ˜แƒก แƒ›แƒ˜แƒฎแƒ”แƒ“แƒ•แƒ˜แƒ—, แƒ แƒแƒ›แƒ”แƒšแƒ˜แƒช แƒแƒ แƒฉแƒ”แƒฃแƒšแƒ˜แƒ.. แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒกแƒแƒ•แƒแƒ แƒแƒฃแƒ“แƒ แƒ“แƒ แƒ แƒ›แƒ˜แƒ—แƒ˜แƒ—แƒ”แƒ‘แƒฃแƒšแƒ˜แƒ แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒก แƒžแƒ แƒแƒชแƒ”แƒกแƒจแƒ˜, แƒแƒกแƒ” แƒ แƒแƒ› แƒจแƒแƒžแƒ˜แƒœแƒ’แƒ˜แƒก แƒ“แƒ แƒแƒก แƒ˜แƒงแƒแƒ•แƒ˜แƒ— แƒฃแƒงแƒฃแƒ แƒแƒ“แƒฆแƒ”แƒ‘แƒ.

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  • แƒจแƒ”แƒ–แƒฆแƒฃแƒ“แƒ•แƒ”แƒ‘แƒ˜: แƒ’แƒ—แƒฎแƒแƒ•แƒ—, แƒ’แƒแƒ˜แƒ—แƒ•แƒแƒšแƒ˜แƒกแƒฌแƒ˜แƒœแƒแƒ—, แƒ แƒแƒ› แƒกแƒแƒ–แƒฆแƒ•แƒแƒ แƒ’แƒแƒ แƒ”แƒ— แƒ’แƒแƒ’แƒ–แƒแƒ•แƒœแƒ˜แƒšแƒ˜ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜แƒกแƒ—แƒ•แƒ˜แƒก แƒ›แƒฌแƒแƒ แƒ›แƒแƒ”แƒ‘แƒšแƒ˜แƒก แƒ’แƒแƒ แƒแƒœแƒขแƒ˜แƒ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒ›แƒแƒฅแƒ›แƒ”แƒ“แƒ˜; แƒ›แƒฌแƒแƒ แƒ›แƒแƒ”แƒ‘แƒšแƒ˜แƒก แƒ›แƒแƒ›แƒกแƒแƒฎแƒฃแƒ แƒ”แƒ‘แƒ˜แƒก แƒžแƒแƒ แƒแƒ›แƒ”แƒขแƒ แƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜; แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ˜แƒœแƒกแƒขแƒ แƒฃแƒฅแƒชแƒ˜แƒ”แƒ‘แƒ˜ แƒ“แƒ แƒฃแƒกแƒแƒคแƒ แƒ—แƒฎแƒแƒ”แƒ‘แƒ˜แƒก แƒ’แƒแƒคแƒ แƒ—แƒฎแƒ˜แƒšแƒ”แƒ‘แƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒ”แƒœแƒแƒ–แƒ”; แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ (แƒ“แƒ แƒ›แƒแƒกแƒ—แƒแƒœ แƒ“แƒแƒ™แƒแƒ•แƒจแƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ›แƒแƒกแƒแƒšแƒ”แƒ‘แƒ˜) แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒจแƒ”แƒ›แƒฃแƒจแƒแƒ•แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒกแƒขแƒแƒœแƒ“แƒแƒ แƒขแƒ”แƒ‘แƒ˜แƒก, แƒกแƒžแƒ”แƒชแƒ˜แƒคแƒ˜แƒ™แƒแƒชแƒ˜แƒ”แƒ‘แƒ˜แƒกแƒ แƒ“แƒ แƒ›แƒแƒ แƒ™แƒ˜แƒ แƒ”แƒ‘แƒ˜แƒก แƒ›แƒแƒ—แƒฎแƒแƒ•แƒœแƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒกแƒแƒ‘แƒแƒ›แƒ˜แƒกแƒแƒ“; แƒ“แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒจแƒ”แƒ”แƒกแƒแƒ‘แƒแƒ›แƒ”แƒ‘แƒแƒ“แƒ”แƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒซแƒแƒ‘แƒ•แƒ˜แƒกแƒ แƒ“แƒ แƒกแƒฎแƒ•แƒ แƒ”แƒšแƒ”แƒฅแƒขแƒ แƒ แƒกแƒขแƒแƒœแƒ“แƒแƒ แƒขแƒ”แƒ‘แƒก (แƒกแƒแƒญแƒ˜แƒ แƒแƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒ›แƒ—แƒฎแƒ•แƒ”แƒ•แƒแƒจแƒ˜ แƒกแƒแƒญแƒ˜แƒ แƒแƒ แƒแƒ“แƒแƒžแƒขแƒ”แƒ แƒ˜ แƒแƒœ แƒ’แƒแƒ“แƒแƒ›แƒงแƒ•แƒแƒœแƒ˜). แƒ›แƒ˜แƒ›แƒฆแƒ”แƒ‘แƒ˜ แƒžแƒแƒกแƒฃแƒฎแƒ˜แƒกแƒ›แƒ’แƒ”แƒ‘แƒ”แƒšแƒ˜แƒ แƒฃแƒ–แƒ แƒฃแƒœแƒ•แƒ”แƒšแƒงแƒแƒก, แƒ แƒแƒ› แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒ™แƒแƒœแƒแƒœแƒ˜แƒ”แƒ แƒแƒ“ แƒจแƒ”แƒ›แƒแƒ˜แƒขแƒแƒœแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒแƒœแƒแƒจแƒ˜. Ubuy-แƒก แƒแƒœ แƒ›แƒ˜แƒกแƒ˜ แƒแƒคแƒ˜แƒšแƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒžแƒ˜แƒ แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒจแƒ•แƒ”แƒแƒ‘แƒ˜แƒ— แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒกแƒแƒก แƒ›แƒ˜แƒ›แƒฆแƒ”แƒ‘แƒ˜ แƒแƒ แƒ˜แƒก แƒ แƒ”แƒ’แƒ˜แƒกแƒขแƒ แƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ˜แƒ›แƒžแƒแƒ แƒขแƒ˜แƒแƒ แƒ˜ แƒ“แƒ แƒ›แƒแƒœ แƒฃแƒœแƒ“แƒ แƒ’แƒแƒ˜แƒ—แƒ•แƒแƒšแƒ˜แƒกแƒฌแƒ˜แƒœแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒงแƒ•แƒ”แƒšแƒ แƒ™แƒแƒœแƒแƒœแƒ˜ แƒ“แƒ แƒ“แƒ”แƒ‘แƒฃแƒšแƒ”แƒ‘แƒ.
  • Ubuy-แƒ–แƒ” แƒฉแƒแƒ›แƒแƒ—แƒ•แƒšแƒ˜แƒšแƒ˜ แƒงแƒ•แƒ”แƒšแƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒแƒ  แƒแƒ แƒ˜แƒก แƒ’แƒแƒงแƒ˜แƒ“แƒ•แƒแƒจแƒ˜, แƒ แƒแƒ“แƒ’แƒแƒœ Ubuy แƒแƒ แƒ˜แƒก แƒ’แƒšแƒแƒ‘แƒแƒšแƒฃแƒ แƒ˜ แƒกแƒแƒซแƒ˜แƒ”แƒ‘แƒ แƒกแƒ˜แƒกแƒขแƒ”แƒ›แƒ แƒ“แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ แƒ”แƒฅแƒ•แƒ”แƒ›แƒ“แƒ”แƒ‘แƒแƒ แƒ”แƒ‘แƒ แƒกแƒแƒ”แƒฅแƒกแƒžแƒแƒ แƒขแƒ/แƒกแƒแƒ•แƒแƒญแƒ แƒ แƒ แƒ”แƒ’แƒฃแƒšแƒแƒชแƒ˜แƒ”แƒ‘แƒก.

GEL 167

แƒจแƒ”แƒฃแƒ™แƒ•แƒ”แƒ—แƒ”แƒ— แƒแƒฎแƒšแƒแƒ•แƒ” แƒ“แƒ แƒ›แƒ˜แƒ˜แƒฆแƒ”แƒ— แƒจแƒแƒ‘แƒแƒ—แƒ˜, แƒแƒฅแƒขแƒแƒ›แƒ‘แƒ”แƒ แƒ˜ 10

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