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Feature engineering for machine learning: Principles and techniques for data scientists

By:

Zheng, Alice

Contributor(s):

Casari, Amanda

Material type: Text

Text

TextPublication details: New Delhi: O' Reilly, ©2018Edition: 1stDescription: xiii, 200pISBN:

9789352137114

Subject(s):

DDC classification:

006.31 ZHE

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Reference Books	GCET LIBRARY B. Tech (AI)	Artificial intelligence	006.31 ZHE (Browse shelf(Opens below))	Not For Loan		072112
Reference Books	GCET LIBRARY B. Tech (AI)	Artificial intelligence	006.31 ZHE (Browse shelf(Opens below))	Not For Loan		072113
Books	GCET LIBRARY B. Tech (AI)	Artificial intelligence	006.31 ZHE (Browse shelf(Opens below))	Available		072114
Books	GCET LIBRARY B. Tech (AI)	Artificial intelligence	006.31 ZHE (Browse shelf(Opens below))	Available		072115
Books	GCET LIBRARY B. Tech (AI)	Artificial intelligence	006.31 ZHE (Browse shelf(Opens below))	Available		072116

Browsing GCET LIBRARY shelves, Shelving location: B. Tech (AI), Collection: Artificial intelligence Close shelf browser (Hides shelf browser)

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Previous	006.31 TAT Hands-on ensemble learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques	006.31 ZHE Feature engineering for machine learning: Principles and techniques for data scientists	006.31 ZHE Feature engineering for machine learning: Principles and techniques for data scientists	006.31 ZHE Feature engineering for machine learning: Principles and techniques for data scientists	006.31015195 DAN Statistics for machine learning: Techniques for exploring supervised, unsupervised and reinforcement learning models with Python and R	006.31015195 DAN Statistics for machine learning: Techniques for exploring supervised, unsupervised and reinforcement learning models with Python and R	006.32 ATI Advanced deep learning with keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients.	Next

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