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Machine Learning with Low Code

The book presents in detail three problem-oriented types of machine learning (Machine Learning, ML): automated learning AutoML without coding, learning BigQuery ML with low coding, and learning using custom code based on scikit-learn and Keras libraries. It does not require... the reader to have deep prior knowledge in ML or programming, but basic skills in these areas will be helpful. Specialized libraries, ML frameworks, GitHub repository, and other tools are described as needed. In each specific case, key ML concepts are revealed using real datasets to solve real problems. The application of AutoML for sales forecasting, the use of BigQuery ML for training linear regression models, training custom ML models in Python for customer churn prediction, and several other popular business cases have been considered.

Author: Гвендолин Стриплинг, Майкл Абель

Printhouse: BHV

Age restrictions: 16+

Year of publication: 2025

ISBN: 9786010847255

Number of pages: 296

Size: 232х165х15 mm

Cover type: soft

Weight: 392 g

ID: 1738633

The book presents in detail three problem-oriented types of machine learning (Machine Learning, ML): automated learning AutoML without coding, learning BigQuery ML with low coding, and learning using custom code based on scikit-learn and Keras libraries. It does not require the reader to have deep prior knowledge in ML or programming, but basic skills in these areas will be helpful. Specialized libraries, ML frameworks, GitHub repository, and other tools are described as needed. In each specific case, key ML concepts are revealed using real datasets to solve real problems. The application of AutoML for sales forecasting, the use of BigQuery ML for training linear regression models, training custom ML models in Python for customer churn prediction, and several other popular business cases have been considered.

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In stock

€ 20.09