Machine learning with R: learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the era of "big data" and "data science." Given the growing prominence of R - a cross-platform, zero-cost statistical programming environm...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing
2023
|
| Ausgabe: | Fourth edition |
| Schriftenreihe: | Expert insight
|
| Schlagworte: | |
| Zusammenfassung: | Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the era of "big data" and "data science." Given the growing prominence of R - a cross-platform, zero-cost statistical programming environment - there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from data. This book explores machine learning with R using clear and practical examples. -- Adapted from publisher's description |
| Beschreibung: | Print on demand edition Previous edition: 2019 |
| Beschreibung: | xxi, 737 Seiten Illustrationen 24 cm |
| ISBN: | 9781801071321 1801071322 |
Internformat
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| 245 | 1 | 0 | |a Machine learning with R |b learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data |c Brett Lantz |
| 250 | |a Fourth edition | ||
| 259 | |a 4 | ||
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| 500 | |a Previous edition: 2019 | ||
| 505 | 8 | |a Introducing Machine Learning -- Managing and Understanding Data -- Lazy Learning : Classification Using Nearest Neighbors -- Probabilistic Learning : Classification Using Naive Bayes -- Divide and Conquer : Classification Using Decision Trees and Rules -- Forecasting Numeric Data : Regression Methods -- Black-Box Methods : Neural Networks and Support Vector Machines -- Finding Patterns : Market Basket Analysis Using Association Rules -- Finding Groups of Data : Clustering with k-means -- Evaluating Model Performance -- Being Successful with Machine Learning -- Advanced Data Preparation -- Challenging Data : Too Much, Too Little, Too Complex -- Building Better Learners -- Making Use of Big Data | |
| 520 | 3 | |a Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the era of "big data" and "data science." Given the growing prominence of R - a cross-platform, zero-cost statistical programming environment - there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from data. This book explores machine learning with R using clear and practical examples. -- Adapted from publisher's description | |
| 653 | 0 | |a Machine learning | |
| 653 | 0 | |a R (Computer program language) | |
| 653 | 0 | |a Apprentissage automatique | |
| 653 | 0 | |a R (Langage de programmation) | |
| 653 | 0 | |a Machine learning | |
| 653 | 0 | |a R (Computer program language) | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-80107-605-0 |w (DE-604)BV049020368 |
| 787 | 0 | 8 | |i Überarbeitung von |b 3. ed. |d 2019 |z 978-1-78829-586-4 |w (DE-604)BV045884198 |
Datensatz im Suchindex
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| author | Lantz, Brett |
| author_GND | (DE-588)1060647117 |
| author_facet | Lantz, Brett |
| author_role | aut |
| author_sort | Lantz, Brett |
| author_variant | b l bl |
| building | Verbundindex |
| bvnumber | BV049362141 |
| classification_rvk | ST 300 ST 250 ST 601 |
| contents | Introducing Machine Learning -- Managing and Understanding Data -- Lazy Learning : Classification Using Nearest Neighbors -- Probabilistic Learning : Classification Using Naive Bayes -- Divide and Conquer : Classification Using Decision Trees and Rules -- Forecasting Numeric Data : Regression Methods -- Black-Box Methods : Neural Networks and Support Vector Machines -- Finding Patterns : Market Basket Analysis Using Association Rules -- Finding Groups of Data : Clustering with k-means -- Evaluating Model Performance -- Being Successful with Machine Learning -- Advanced Data Preparation -- Challenging Data : Too Much, Too Little, Too Complex -- Building Better Learners -- Making Use of Big Data |
| ctrlnum | (OCoLC)1427328842 (DE-599)BVBBV049362141 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | Fourth edition |
| format | Book |
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| id | DE-604.BV049362141 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:52:14Z |
| indexdate | 2024-07-20T08:02:36Z |
| institution | BVB |
| isbn | 9781801071321 1801071322 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034622288 |
| oclc_num | 1427328842 |
| open_access_boolean | |
| owner | DE-521 DE-1049 |
| owner_facet | DE-521 DE-1049 |
| physical | xxi, 737 Seiten Illustrationen 24 cm |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Packt Publishing |
| record_format | marc |
| series2 | Expert insight |
| spelling | Lantz, Brett Verfasser (DE-588)1060647117 aut Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data Brett Lantz Fourth edition 4 Birmingham Packt Publishing 2023 xxi, 737 Seiten Illustrationen 24 cm txt rdacontent sti rdacontent n rdamedia nc rdacarrier Expert insight Print on demand edition Previous edition: 2019 Introducing Machine Learning -- Managing and Understanding Data -- Lazy Learning : Classification Using Nearest Neighbors -- Probabilistic Learning : Classification Using Naive Bayes -- Divide and Conquer : Classification Using Decision Trees and Rules -- Forecasting Numeric Data : Regression Methods -- Black-Box Methods : Neural Networks and Support Vector Machines -- Finding Patterns : Market Basket Analysis Using Association Rules -- Finding Groups of Data : Clustering with k-means -- Evaluating Model Performance -- Being Successful with Machine Learning -- Advanced Data Preparation -- Challenging Data : Too Much, Too Little, Too Complex -- Building Better Learners -- Making Use of Big Data Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the era of "big data" and "data science." Given the growing prominence of R - a cross-platform, zero-cost statistical programming environment - there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from data. This book explores machine learning with R using clear and practical examples. -- Adapted from publisher's description Machine learning R (Computer program language) Apprentissage automatique R (Langage de programmation) Erscheint auch als Online-Ausgabe 978-1-80107-605-0 (DE-604)BV049020368 Überarbeitung von 3. ed. 2019 978-1-78829-586-4 (DE-604)BV045884198 |
| spellingShingle | Lantz, Brett Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data Introducing Machine Learning -- Managing and Understanding Data -- Lazy Learning : Classification Using Nearest Neighbors -- Probabilistic Learning : Classification Using Naive Bayes -- Divide and Conquer : Classification Using Decision Trees and Rules -- Forecasting Numeric Data : Regression Methods -- Black-Box Methods : Neural Networks and Support Vector Machines -- Finding Patterns : Market Basket Analysis Using Association Rules -- Finding Groups of Data : Clustering with k-means -- Evaluating Model Performance -- Being Successful with Machine Learning -- Advanced Data Preparation -- Challenging Data : Too Much, Too Little, Too Complex -- Building Better Learners -- Making Use of Big Data |
| title | Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data |
| title_auth | Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data |
| title_exact_search | Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data |
| title_exact_search_txtP | Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data |
| title_full | Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data Brett Lantz |
| title_fullStr | Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data Brett Lantz |
| title_full_unstemmed | Machine learning with R learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data Brett Lantz |
| title_short | Machine learning with R |
| title_sort | machine learning with r learn techniques for building and improving machine learning models from data preparation to model tuning evaluation and working with big data |
| title_sub | learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data |
| work_keys_str_mv | AT lantzbrett machinelearningwithrlearntechniquesforbuildingandimprovingmachinelearningmodelsfromdatapreparationtomodeltuningevaluationandworkingwithbigdata |