Practical Machine Learning Illustrated with KNIME [electronic resource] / by Yu Geng, Qin Li, Geng Yang, Wan Qiu.

Por: Geng, Yu [author.]Colaborador(es): Li, Qin [author.] | Yang, Geng [author.] | Qiu, Wan [author.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XIV, 304 p. 392 illus., 358 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819739547Tema(s): Machine learning | Artificial intelligence -- Data processing | Machine Learning | Data ScienceFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.31 Clasificación LoC:Q325.5-.7Recursos en línea: Libro electrónicoTexto
Contenidos:
Chapter 1 Overview of Artificial Intelligence and Machine Learning -- Chapter 2　Basic Knowledge of Machine Learning -- Chapter 3　Linear Regression -- Chapter 4　Logistic Regression -- Chapter 5　Model Optimization -- Chapter 6 Support Vector Machine -- Chapter 7 Decision Tree -- Chapter 8　Understanding of Decision Tree -- Chapter 9　Bayesian Analysis -- Chapter 10　Deep Learning.
En: Springer Nature eBookResumen: This book guides professionals and students from various backgrounds to use machine learning in their own fields with low-code platform KNIME and without coding. Many people from various industries need use machine learning to solve problems in their own domains. However, machine learning is often viewed as the domain of programmers, especially for those who are familiar with Python. It is too hard for people from different backgrounds to learn Python to use machine learning. KNIME, the low-code platform, comes to help. KNIME helps people use machine learning in an intuitive environment, enabling everyone to focus on what to do instead of how to do. This book helps the readers gain an intuitive understanding of the basic concepts of machine learning through illustrations to practice machine learning in their respective fields. The author provides a practical guide on how to participate in Kaggle completions with KNIME to practice machine learning techniques.
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Chapter 1 Overview of Artificial Intelligence and Machine Learning -- Chapter 2　Basic Knowledge of Machine Learning -- Chapter 3　Linear Regression -- Chapter 4　Logistic Regression -- Chapter 5　Model Optimization -- Chapter 6 Support Vector Machine -- Chapter 7 Decision Tree -- Chapter 8　Understanding of Decision Tree -- Chapter 9　Bayesian Analysis -- Chapter 10　Deep Learning.

This book guides professionals and students from various backgrounds to use machine learning in their own fields with low-code platform KNIME and without coding. Many people from various industries need use machine learning to solve problems in their own domains. However, machine learning is often viewed as the domain of programmers, especially for those who are familiar with Python. It is too hard for people from different backgrounds to learn Python to use machine learning. KNIME, the low-code platform, comes to help. KNIME helps people use machine learning in an intuitive environment, enabling everyone to focus on what to do instead of how to do. This book helps the readers gain an intuitive understanding of the basic concepts of machine learning through illustrations to practice machine learning in their respective fields. The author provides a practical guide on how to participate in Kaggle completions with KNIME to practice machine learning techniques.

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