Applied Supervised Learning with Python (notice n° 71856)

détails MARC
000 -LEADER
fixed length control field 03279cam a2200289zu 4500
003 - CONTROL NUMBER IDENTIFIER
control field FRCYB88869945
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250107231900.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250108s2019 fr | o|||||0|0|||eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781789954920
035 ## - SYSTEM CONTROL NUMBER
System control number FRCYB88869945
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 Johnston, Benjamin
245 01 - TITLE STATEMENT
Title Applied Supervised Learning with Python
Statement of responsibility, etc. ['Johnston, Benjamin', 'Mathur, Ishita']
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 2019
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. Explore the exciting world of machine learning with the fastest growing technology in the world Key Features Understand various machine learning concepts with real-world examples Implement a supervised machine learning pipeline from data ingestion to validation Gain insights into how you can use machine learning in everyday life Book Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learn Understand the concept of supervised learning and its applications Implement common supervised learning algorithms using machine learning Python libraries Validate models using the k-fold technique Build your models with decision trees to get results effortlessly Use ensemble modeling techniques to improve the performance of your model Apply a variety of metrics to compare machine learning models Who this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Johnston, Benjamin
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Mathur, Ishita
856 40 - ELECTRONIC LOCATION AND ACCESS
Access method Cyberlibris
Uniform Resource Identifier <a href="https://international.scholarvox.com/netsen/book/88869945">https://international.scholarvox.com/netsen/book/88869945</a>
Electronic format type text/html
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