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 Online-Ressource
Titel:Python for data science and machine learning
Titelzusatz:zero to hero
Institutionen:Meta Brains (Firm), [Präsentator]   i
 Manning (Firm), [Verlag]   i
Ausgabe:[First edition].
Verlagsort:[Place of publication not identified]
Verlag:Manning Publications
Jahr:2023
Umfang:1 online resource (1 video file (6 hr., 1 min.))
Illustrationen:sound, color.
Fussnoten:Online resource; title from title details screen (O'Reilly, viewed June 13, 2023)
Abstract:This A to Z course introduces newcomers to the world of data science and teaches the fundamental skills for using machine learning and artificial intelligence (AI) to glean meaning and insights from data. It covers Python's data types and shows how to use the must-have Python data science libraries, including Pandas for data analysis and Matplotlib for creating visuals of the results. Once you understand how to format and clean your data and perform exploratory data analysis, we move to the machine learning side. Here, we introduce you to supervised vs unsupervised learning, as well as the core algorithms, including simple and multiple linear regression. We finish up with a deep dive into a recommender system for movies, and a chance to put together all your new skills and knowledge. Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it, learning and applying these algorithms in the real world. The data science field is lucrative and growing. This course will introduce you to all the foundational skills that a data scientist must have. If you have no background in statistics, don't let that stop you from enrolling in this course!.
URL:Aggregator: https://learning.oreilly.com/library/view/-/10000DIVC202341/?ar
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Instructional films
 Nonfiction films
 Internet videos
K10plus-PPN:1850986215
 
 
Lokale URL UB: Zum Volltext
 
 Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
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Bibliothek/Idn:UW / m4343666530
Lokale URL Inst.: Zum Volltext

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