Machine learning for text and image data analysis

By:

Motwani, Bharti

Material type: Text

TextPublication details: New Delhi | Wiley India Pvt. Ltd. | 2023Description: xxii, 805pISBN:

9789354643606

Subject(s):

DDC classification:

006.31 MOT

Summary: Machine Learning for Text and Image Data Analysis: Practical Approach with Business Use Cases enables readers gain sufficient knowledge and experience to perform analysis for text and image data using different machine learning techniques available in Python. The objective is to explain the concepts and to simultaneously develop in readers an understanding of its application with case-based methodology. The book attempts to provide more meaningful and easier learning experience, it has been written with more interesting and relevant real-life examples.

Item type:

Book

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Book	IIM Kashipur	006.31 MOT (Browse shelf(Opens below))	Available		10876

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Previous	006.31 Deep learning and scientific computing with R torch /	006.31 DEE Deep learning in computer vision: principles and applications/	006.31 GEN Generalization with deep learning: for improvement on sensing capability/	006.31 MOT Machine learning for text and image data analysis : practical approach with business use cases	006.31 MUR Machine learning: a probabilistic perspective/	006.312 ATK Text analytics: an introduction to the science and applications of unstructured information analysis/	006.312 BRE Introduction to data system: building from python/	Next

Includes index.

Machine Learning for Text and Image Data Analysis: Practical Approach with Business Use Cases enables readers gain sufficient knowledge and experience to perform analysis for text and image data using different machine learning techniques available in Python. The objective is to explain the concepts and to simultaneously develop in readers an understanding of its application with case-based methodology. The book attempts to provide more meaningful and easier learning experience, it has been written with more interesting and relevant real-life examples.

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