Python Data Science Essentials (notice n° 70937)

détails MARC
000 -LEADER
fixed length control field 03057cam a2200289zu 4500
003 - CONTROL NUMBER IDENTIFIER
control field FRCYB88863263
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250107230842.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250108s2018 fr | o|||||0|0|||eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781789537864
035 ## - SYSTEM CONTROL NUMBER
System control number FRCYB88863263
040 ## - CATALOGING SOURCE
Original cataloging agency FR-PaCSA
Language of cataloging en
Transcribing agency
Description conventions rda
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Boschetti, Alberto
245 01 - TITLE STATEMENT
Title Python Data Science Essentials
Statement of responsibility, etc. ['Boschetti, Alberto', 'Massaron, Luca']
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer Packt Publishing
Date of production, publication, distribution, manufacture, or copyright notice 2018
300 ## - PHYSICAL DESCRIPTION
Extent p.
336 ## - CONTENT TYPE
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type code c
Source rdamdedia
338 ## - CARRIER TYPE
Carrier type code c
Source rdacarrier
520 ## - SUMMARY, ETC.
Summary, etc. Gain useful insights from your data using popular data science tools Key Features A one-stop guide to Python libraries such as pandas and NumPy Comprehensive coverage of data science operations such as data cleaning and data manipulation Choose scalable learning algorithms for your data science tasks Book Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You'll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learn Set up your data science toolbox on Windows, Mac, and Linux Use the core machine learning methods offered by the scikit-learn library Manipulate, fix, and explore data to solve data science problems Learn advanced explorative and manipulative techniques to solve data operations Optimize your machine learning models for optimized performance Explore and cluster graphs, taking advantage of interconnections and links in your data Who this book is for If you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Boschetti, Alberto
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Massaron, Luca
856 40 - ELECTRONIC LOCATION AND ACCESS
Access method Cyberlibris
Uniform Resource Identifier <a href="https://international.scholarvox.com/netsen/book/88863263">https://international.scholarvox.com/netsen/book/88863263</a>
Electronic format type text/html
Host name

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