Data science solutions with python: (Record no. 6191)

MARC details
000 -LEADER
fixed length control field 02294nam a22001937a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240219185318.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240219b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781484283509
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number NOK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Nokeri, Tshepo Chris
245 ## - TITLE STATEMENT
Title Data science solutions with python:
Remainder of title fast and scalable models using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Apress
Place of publication, distribution, etc. New York
Date of publication, distribution, etc. 2024
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 119 p.
365 ## - TRADE PRICE
Price type code INR
Price amount 499.00
520 ## - SUMMARY, ETC.
Summary, etc. Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. <br/><br/><br/>The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras.<br/><br/>The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.<br/><br/>This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. <br/><br/>(https://link.springer.com/book/10.1007/978-1-4842-7762-1#about-this-book)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Date last seen Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     IT & Decisions Sciences SBHPL/INV/1162/2023-2024 27-01-2024 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 02/19/2024 Sarat Book House Pvt. Ltd. 346.81   006.31 NOK 005979 02/19/2024 1 499.00 02/19/2024 Book

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