Data Science and Machine Learning Using Python | Reema Thareja | 2nd Edition | McGraw Hill Publications ( English Medium )

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Data Science and Machine Learning Using Python | Reema Thareja | 2nd Edition | McGraw Hill Publications ( English Medium )

The second edition of "Data Science and Machine Learning Using Python" by Reema Thareja is a comprehensive guide for anyone looking to delve into the world of data science and machine learning using the Python programming language. This book is designed to provide a practical and hands-on approach to learning these complex topics, making it suitable for both beginners and experienced professionals in the field. The book covers a wide range of topics, starting with an introduction to data science and machine learning concepts. It then moves on to cover essential Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn that are commonly used in data science projects. The author provides detailed explanations and examples to help readers understand how to use these libraries effectively. One of the key features of this book is its focus on real-world applications of data science and machine learning. The author includes numerous case studies and examples that demonstrate how these techniques can be applied in various industries such as healthcare, finance, marketing, and more. Readers will learn how to build predictive models, analyze large datasets, visualize data, and make informed decisions based on their findings. In addition to covering the technical aspects of data science and machine learning, the book also addresses important ethical considerations related to handling sensitive data. The author emphasizes the importance of maintaining privacy and security when working with personal information or proprietary datasets. Overall, "Data Science and Machine Learning Using Python" offers a comprehensive introduction to these cutting-edge technologies in an accessible manner. Whether you are a student looking to expand your knowledge or a professional seeking practical guidance, this book has something for everyone interested in leveraging the power of data science and machine learning.

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Product Details

SKU / BOOK Code:	McGH-DSML-2E-RT(E)
Publisher:	McGraw Hill Publications
Author:	Reema Thareja
Binding Type:	Paperback
No. of Pages:	736
ISBN-10:	9364448774
ISBN-13:	978-9364448772
Edition:	2nd Edition
Language:	English Medium
Publish Year:	2025-11
Weight (g):	1952
Product Condition:	New
Reading Age:	Above 18 Years
Country of Origin:	India
Genre:	Computers & Internet
Manufacturer:	McGraw Hill Publications
Importer:	McGraw Hill Publications
Packer:	Fullfilled by Supplier

Product Description

Data Science and Machine Learning Using Python | Reema Thareja | 2nd Edition | McGraw Hill Publications ( English Medium )

The second edition of "Data Science and Machine Learning Using Python" by Reema Thareja is a comprehensive guide for anyone looking to delve into the world of data science and machine learning using the Python programming language. This book is designed to provide a practical and hands-on approach to learning these complex topics, making it suitable for both beginners and experienced professionals in the field. The book covers a wide range of topics, starting with an introduction to data science and machine learning concepts. It then moves on to cover essential Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn that are commonly used in data science projects. The author provides detailed explanations and examples to help readers understand how to use these libraries effectively. One of the key features of this book is its focus on real-world applications of data science and machine learning. The author includes numerous case studies and examples that demonstrate how these techniques can be applied in various industries such as healthcare, finance, marketing, and more. Readers will learn how to build predictive models, analyze large datasets, visualize data, and make informed decisions based on their findings. In addition to covering the technical aspects of data science and machine learning, the book also addresses important ethical considerations related to handling sensitive data. The author emphasizes the importance of maintaining privacy and security when working with personal information or proprietary datasets. Overall, "Data Science and Machine Learning Using Python" offers a comprehensive introduction to these cutting-edge technologies in an accessible manner. Whether you are a student looking to expand your knowledge or a professional seeking practical guidance, this book has something for everyone interested in leveraging the power of data science and machine learning.

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