eBook
Applied Statistics with Python : Volume II: Multivariate Models
| Τίτλος: | Applied Statistics with Python : Volume II: Multivariate Models |
|---|---|
| Περιγραφή: | Applied Statistics with Python, Volume II: Multivariate Models focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis.As in Volume I, the Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning. The book relies heavily on tools from the standard sklearn package, which are integrated directly into the discussion. Unlike many other resources, Python is not treated as an add-on, but as an organic part of the learning process.This book is based on the author's 15 years of experience teaching statistics and is designed for undergraduate and first-year graduate students in fields such as business, economics, biology, social sciences, and natural sciences. However, more advanced students and professionals might also find it valuable. While some familiarity with basic statistics is helpful, it is not required - core concepts are introduced and explained along the way, making the material accessible to a wide range of learners.Key Features: Employs Python as an organic part of the learning process. Removes the tedium of hand/calculator computations. Weaves code into the text at every step in a clear and accessible way. Covers advanced machine-learning topics. Uses tools from Standardized sklearn Python package. |
| Συγγραφείς: | Leon Kaganovskiy |
| Resource Type: | eBook. |
| Θέματα: | Python (Computer program language), Multivariate analysis--Data processing, Statistics--Data processing |
| Categories: | MATHEMATICS / Probability & Statistics / General, COMPUTERS / Mathematical & Statistical Software |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 4348173 RelevancyScore: 987 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 987.310668945313 |
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| Items | – Name: Title Label: Title Group: Ti Data: Applied Statistics with Python : Volume II: Multivariate Models – Name: Abstract Label: Description Group: Ab Data: Applied Statistics with Python, Volume II: Multivariate Models focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis.As in Volume I, the Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning. The book relies heavily on tools from the standard sklearn package, which are integrated directly into the discussion. Unlike many other resources, Python is not treated as an add-on, but as an organic part of the learning process.This book is based on the author's 15 years of experience teaching statistics and is designed for undergraduate and first-year graduate students in fields such as business, economics, biology, social sciences, and natural sciences. However, more advanced students and professionals might also find it valuable. While some familiarity with basic statistics is helpful, it is not required - core concepts are introduced and explained along the way, making the material accessible to a wide range of learners.Key Features: Employs Python as an organic part of the learning process. Removes the tedium of hand/calculator computations. Weaves code into the text at every step in a clear and accessible way. Covers advanced machine-learning topics. Uses tools from Standardized sklearn Python package. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Leon+Kaganovskiy%22">Leon Kaganovskiy</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Python+%28Computer+program+language%29%22">Python (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis--Data+processing%22">Multivariate analysis--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics--Data+processing%22">Statistics--Data processing</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Mathematical+%26+Statistical+Software%22">COMPUTERS / Mathematical & Statistical Software</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=4348173 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 519.502855133 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Python (Computer program language) Type: general – SubjectFull: Multivariate analysis--Data processing Type: general – SubjectFull: Statistics--Data processing Type: general Titles: – TitleFull: Applied Statistics with Python : Volume II: Multivariate Models Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Leon Kaganovskiy – PersonEntity: Name: NameFull: Leon Kaganovskiy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 – D: 29 M: 04 Type: profile Y: 2026 Identifiers: – Type: isbn-print Value: 9781041006251 – Type: isbn-electronic Value: 9781003610830 – Type: isbn-electronic Value: 9781040560310 – Type: isbn-electronic Value: 9781040662120 Numbering: – Type: volume Value: Volume II Titles: – TitleFull: Applied Statistics with Python : Volume II: Multivariate Models Type: main |
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