Python Feature Engineering by Oswald Campesato (8 results)

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Taschenbuch. Condition: Neu. Python 3 and Feature Engineering | Oswald Campesato | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | De Gruyter | EAN 9781683929499 | Verantwortliche Person für die EU: De Gruyter [9], Genthiner Str. 13, 10785 Berlin, orders[at]degruyter[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you'll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you'll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework. 230 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over thirty-five books including Python 3 Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep L.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you'll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you'll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework.Walter de Gruyter, Genthiner Straße 13, 10785 Berlin 230 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you'll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you'll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework.