Mark Levene is emeritus professor of Computer Science at Birkbeck University of London. His main area of expertise encompasses Data Science and Machine Learning, including Applied Machine Learning, Trustworthy and Safe Al. Martyn Harris is a lecturer and Programme Director at Birkbeck University of London. His areas of expertise include, Data Science, Applied Machine Learning, and Natural Language Processing.
Just enough data science and machine learning. Essential tools and techniques
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- Nombre de pages204
- FormatGrand Format
- PrésentationBroché
- Poids0.375 kg
- Dimensions17,8 cm × 23,2 cm × 1,4 cm
- ISBN978-0-13-834074-2
- EAN9780138340742
- Date de parution01/08/2024
- ÉditeurPearson
Résumé
In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject.
The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science.
The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless.
The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science.
The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless.