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Synopsis
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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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB
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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB: Beginner Level
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Teaching Machine Learning Programming in Pandas and Jupyter-Lab
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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB
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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB: Beginner Level
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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB
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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB: Beginner Level
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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB
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ISBN 10: 6205657236
ISBN 13: 9786205657232
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TEACHING MACHINE LEARNING PROGRAMMING IN PANDAS AND JUPYTER-LAB
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 80 pp. Englisch. Seller Inventory # 9786205657232