Utvidet returrett til 31. januar 2025

Scikit-learn in Details

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Om Scikit-learn in Details

This book is a guide for you on how to use Scikit-Learn, a machine learning library for Python programming language. The author first helps you know what Scikit-Learn are and how to set it up on your system. You are also guided on how to load datasets into Scikit-Learn. The author has then guided you on how to use the various machine learning algorithms to implement machine learning models of different types with Scikit-Learn. Some of the algorithms that have been discussed include Support Vector Machine (SVM), Linear Regression, K-Nearest Neighbors and K-Means. In all these, practical examples have been given, hence you will know how to implement models and use them for making predictions. The content is: Getting Started with Scikit-learn Support Vector Machines in Scikit-learn Scikit-Learn Linear Regression Scikit-Learn k-Nearest Neighbors Classifier K-Means Clustering With Scikit-LearnSubjects include: python programming language, python, linear regression book, scikit-learn, scikit-learn and tensorflow, support vector machine, linear regression, k-nearest neighbor, k-means, kernel, linear regression models, data visualisation, linear regression analysis, linear regression machine learning.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9781731040510
  • Bindende:
  • Paperback
  • Sider:
  • 70
  • Utgitt:
  • 8. november 2018
  • Dimensjoner:
  • 152x229x4 mm.
  • Vekt:
  • 104 g.
  • BLACK NOVEMBER
Leveringstid: 2-4 uker
Forventet levering: 8. desember 2024

Beskrivelse av Scikit-learn in Details

This book is a guide for you on how to use Scikit-Learn, a machine learning library for Python programming language. The author first helps you know what Scikit-Learn are and how to set it up on your system. You are also guided on how to load datasets into Scikit-Learn. The author has then guided you on how to use the various machine learning algorithms to implement machine learning models of different types with Scikit-Learn. Some of the algorithms that have been discussed include Support Vector Machine (SVM), Linear Regression, K-Nearest Neighbors and K-Means. In all these, practical examples have been given, hence you will know how to implement models and use them for making predictions. The content is: Getting Started with Scikit-learn Support Vector Machines in Scikit-learn Scikit-Learn Linear Regression Scikit-Learn k-Nearest Neighbors Classifier K-Means Clustering With Scikit-LearnSubjects include: python programming language, python, linear regression book, scikit-learn, scikit-learn and tensorflow, support vector machine, linear regression, k-nearest neighbor, k-means, kernel, linear regression models, data visualisation, linear regression analysis, linear regression machine learning.

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