Utvidet returrett til 31. januar 2025

TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB

TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LABav Jorge Gómez
Om TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB

This book is designed for those who wish to enter the world of machine learning, i.e. beginner mode. Initially, data manipulation through dataframes in the Python programming language, embedded in the Jupyter-lab and Pandas framework, will be addressed. Then data will be extracted from csv files to manage them in dataframes. Indexing, selection and allocation, indexing in Pandas, tag-based selection, conditional selection, data allocation, summary functions, maps and grouping and sorting. Finally, we will proceed to program basic supervised learning models such as linear regression with a single variable, linear regression with multiple variables, saving and loading the training model, data management with dummy variables, separation of the training and test data sets.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9786205657232
  • Bindende:
  • Paperback
  • Sider:
  • 80
  • Utgitt:
  • 31. januar 2023
  • Dimensjoner:
  • 150x5x220 mm.
  • Vekt:
  • 137 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 12. desember 2024

Beskrivelse av TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB

This book is designed for those who wish to enter the world of machine learning, i.e. beginner mode. Initially, data manipulation through dataframes in the Python programming language, embedded in the Jupyter-lab and Pandas framework, will be addressed. Then data will be extracted from csv files to manage them in dataframes. Indexing, selection and allocation, indexing in Pandas, tag-based selection, conditional selection, data allocation, summary functions, maps and grouping and sorting. Finally, we will proceed to program basic supervised learning models such as linear regression with a single variable, linear regression with multiple variables, saving and loading the training model, data management with dummy variables, separation of the training and test data sets.

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