Tag: Supervised

December 9, 2024

Data Science Training – Supervised Learning

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Free Download Data Science Training – Supervised Learning: Ein praktischer Einstieg ins überwachte maschinelle Lernen
Deutsch | 2024 | ISBN: 3662679590 | 595 Pages | PDF (True) | 12 MB
Dieses Lehrbuch erklärt auf narrative und direkte Weise die wichtigen Zusammenhänge zwischen Data Science, Künstlicher Intelligenz und anderen Disziplinen und Domänen wie Datenschutz und Ethik, mit Fokus auf überwachtes Lernen (Supervised Learning).Wir begleiten Anna und Karl während ihrer Traineephase in einer internationalen Versicherung. Schritt für Schritt reifen sie zu Data Scientists, indem sie sich intensiv mit der Titanic-Katastrophe auseinandersetzen. Anna kann Python programmieren, während Karl ein grafisches Werkzeug (KNIME Analytics Platform) benutzt. Bei ihren Untersuchungen stoßen sie auf interessante Fakten und Mythen. Mit Unterstützung von Max und Sophia verarbeiten sie historische Daten, um Vorhersagen zu erstellen (Predictive Analytics). Dabei benutzen sie Methoden und Algorithmen des maschinellen Lernens.Begleitende Zusatzmaterialien (KNIME Workflows, Jupyter Notebooks, Erklärvideos) stehen den Lernenden online zur Verfügung. Und wenn in diesemBuch Anna und Karl sich auf Themen des überwachten Lernens konzentrieren, werden wir künftig mit ihnen noch weitere Gebiete der Data Science entdecken.

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September 24, 2024

The Mathematics of Machine Learning Lectures on Supervised Methods and Beyond (De Gruyter Textbook)

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Free Download The Mathematics of Machine Learning: Lectures on Supervised Methods and Beyond (De Gruyter Textbook) by Maria Han Veiga, François Gaston Ged
English | May 20, 2024 | ISBN: 3111288471 | MOBI | 3.66 Mb
This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics.

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September 16, 2024

Partially Supervised Learning Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected

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Free Download Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers By Gabriel B. P. Costa, Moacir Ponti, Alejandro C. Frery (auth.), Zhi-Hua Zhou, Friedhelm Schwenker (eds.)
2013 | 117 Pages | ISBN: 3642407048 | PDF | 3 MB
This book constitutes the thoroughly refereed revised selected papers from the Second IAPR International Workshop, PSL 2013, held in Nanjing, China, in May 2013. The 10 papers included in this volume were carefully reviewed and selected from 26 submissions. Partially supervised learning is a rapidly evolving area of machine learning. It generalizes many kinds of learning paradigms including supervised and unsupervised learning, semi-supervised learning for classification and regression, transductive learning, semi-supervised clustering, multi-instance learning, weak label learning, policy learning in partially observable environments, etc.

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September 15, 2024

Fundamentals of Supervised Machine Learning With Applications in Python, R, and Stata (Statistics and Computing)

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Free Download Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata (Statistics and Computing) by Giovanni Cerulli
English | November 15, 2023 | ISBN: 3031413369 | 420 pages | MOBI | 63 Mb
This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over different software platforms.

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April 26, 2024

Supervised and Unsupervised Data Engineering for Multimedia Data (EPUB)

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Free Download Supervised and Unsupervised Data Engineering for Multimedia Data
English | 2024 | ISBN: 1119786347 | 372 Pages | EPUB | 7 MB
Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go-to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting-edge methodologies.

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