Python feature engineering cookbook: over 70 recipes for creating, engineering, and transforming features to build machine learning models
bExtract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries/b h4Key Features/h4 ulliDiscover solutions for feature generation, feature extraction, and feature selection /li liUncover the end-to-end feature engineering pr...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2020
|
| Ausgabe: | 1 |
| Schlagworte: | |
| Online-Zugang: | UBY01 |
| Zusammenfassung: | bExtract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries/b h4Key Features/h4 ulliDiscover solutions for feature generation, feature extraction, and feature selection /li liUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets /li liImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries/li/ul h4Book Description/h4 Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you'll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You'll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you'll have discovered tips and practical solutions to all of your feature engineering problems. h4What you will learn/h4 ulliSimplify your feature engineering pipelines with powerful Python packages /li liGet to grips with imputing missing values /li liEncode categorical variables with a wide set of techniques /li liExtract insights from text quickly and effortlessly /li liDevelop features from transactional data and time series data /li liDerive new features by combining existing variables /li liUnderstand how to transform, discretize, and scale your variables /li liCreate informative variables from date and time/li/ul h4Who this book is for/h4 This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book |
| Beschreibung: | 1 Online-Ressource (372 Seiten) |
| ISBN: | 9781789807820 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV047069726 | ||
| 003 | DE-604 | ||
| 005 | 20210702 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 201218s2020 |||| o||u| ||||||eng d | ||
| 020 | |a 9781789807820 |9 978-1-78980-782-0 | ||
| 035 | |a (ZDB-5-WPSE)9781789807820372 | ||
| 035 | |a (ZDB-4-NLEBK)2358819 | ||
| 035 | |a (OCoLC)1227476116 | ||
| 035 | |a (DE-599)BVBBV047069726 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-706 | ||
| 100 | 1 | |a Galli, Soledad |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Python feature engineering cookbook |b over 70 recipes for creating, engineering, and transforming features to build machine learning models |c Galli, Soledad |
| 250 | |a 1 | ||
| 264 | 1 | |a Birmingham |b Packt Publishing Limited |c 2020 | |
| 300 | |a 1 Online-Ressource (372 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 520 | |a bExtract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries/b h4Key Features/h4 ulliDiscover solutions for feature generation, feature extraction, and feature selection /li liUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets /li liImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries/li/ul h4Book Description/h4 Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you'll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. | ||
| 520 | |a You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You'll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you'll have discovered tips and practical solutions to all of your feature engineering problems. | ||
| 520 | |a h4What you will learn/h4 ulliSimplify your feature engineering pipelines with powerful Python packages /li liGet to grips with imputing missing values /li liEncode categorical variables with a wide set of techniques /li liExtract insights from text quickly and effortlessly /li liDevelop features from transactional data and time series data /li liDerive new features by combining existing variables /li liUnderstand how to transform, discretize, and scale your variables /li liCreate informative variables from date and time/li/ul h4Who this book is for/h4 This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book | ||
| 650 | 4 | |a COMPUTERS / Data Processing | |
| 650 | 4 | |a COMPUTERS / Databases / Data Mining | |
| 912 | |a ZDB-5-WPSE |a ZDB-4-NLEBK | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032476752 | ||
| 966 | e | |u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2358819 |l UBY01 |p ZDB-4-NLEBK |q UBY01_DDA21 |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804182071838507008 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Galli, Soledad |
| author_facet | Galli, Soledad |
| author_role | aut |
| author_sort | Galli, Soledad |
| author_variant | s g sg |
| building | Verbundindex |
| bvnumber | BV047069726 |
| collection | ZDB-5-WPSE ZDB-4-NLEBK |
| ctrlnum | (ZDB-5-WPSE)9781789807820372 (ZDB-4-NLEBK)2358819 (OCoLC)1227476116 (DE-599)BVBBV047069726 |
| edition | 1 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03750nmm a2200373zc 4500</leader><controlfield tag="001">BV047069726</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20210702 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">201218s2020 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781789807820</subfield><subfield code="9">978-1-78980-782-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-5-WPSE)9781789807820372</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-4-NLEBK)2358819</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1227476116</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047069726</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-706</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Galli, Soledad</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Python feature engineering cookbook</subfield><subfield code="b">over 70 recipes for creating, engineering, and transforming features to build machine learning models</subfield><subfield code="c">Galli, Soledad</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham</subfield><subfield code="b">Packt Publishing Limited</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (372 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">bExtract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries/b h4Key Features/h4 ulliDiscover solutions for feature generation, feature extraction, and feature selection /li liUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets /li liImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries/li/ul h4Book Description/h4 Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you'll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You'll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you'll have discovered tips and practical solutions to all of your feature engineering problems. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">h4What you will learn/h4 ulliSimplify your feature engineering pipelines with powerful Python packages /li liGet to grips with imputing missing values /li liEncode categorical variables with a wide set of techniques /li liExtract insights from text quickly and effortlessly /li liDevelop features from transactional data and time series data /li liDerive new features by combining existing variables /li liUnderstand how to transform, discretize, and scale your variables /li liCreate informative variables from date and time/li/ul h4Who this book is for/h4 This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS / Data Processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS / Databases / Data Mining</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-5-WPSE</subfield><subfield code="a">ZDB-4-NLEBK</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032476752</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2358819</subfield><subfield code="l">UBY01</subfield><subfield code="p">ZDB-4-NLEBK</subfield><subfield code="q">UBY01_DDA21</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047069726 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:13:33Z |
| indexdate | 2024-07-10T09:01:44Z |
| institution | BVB |
| isbn | 9781789807820 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032476752 |
| oclc_num | 1227476116 |
| open_access_boolean | |
| owner | DE-706 |
| owner_facet | DE-706 |
| physical | 1 Online-Ressource (372 Seiten) |
| psigel | ZDB-5-WPSE ZDB-4-NLEBK ZDB-4-NLEBK UBY01_DDA21 |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Packt Publishing Limited |
| record_format | marc |
| spelling | Galli, Soledad Verfasser aut Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models Galli, Soledad 1 Birmingham Packt Publishing Limited 2020 1 Online-Ressource (372 Seiten) txt rdacontent c rdamedia cr rdacarrier bExtract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries/b h4Key Features/h4 ulliDiscover solutions for feature generation, feature extraction, and feature selection /li liUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets /li liImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries/li/ul h4Book Description/h4 Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you'll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You'll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you'll have discovered tips and practical solutions to all of your feature engineering problems. h4What you will learn/h4 ulliSimplify your feature engineering pipelines with powerful Python packages /li liGet to grips with imputing missing values /li liEncode categorical variables with a wide set of techniques /li liExtract insights from text quickly and effortlessly /li liDevelop features from transactional data and time series data /li liDerive new features by combining existing variables /li liUnderstand how to transform, discretize, and scale your variables /li liCreate informative variables from date and time/li/ul h4Who this book is for/h4 This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book COMPUTERS / Data Processing COMPUTERS / Databases / Data Mining |
| spellingShingle | Galli, Soledad Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models COMPUTERS / Data Processing COMPUTERS / Databases / Data Mining |
| title | Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models |
| title_auth | Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models |
| title_exact_search | Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models |
| title_exact_search_txtP | Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models |
| title_full | Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models Galli, Soledad |
| title_fullStr | Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models Galli, Soledad |
| title_full_unstemmed | Python feature engineering cookbook over 70 recipes for creating, engineering, and transforming features to build machine learning models Galli, Soledad |
| title_short | Python feature engineering cookbook |
| title_sort | python feature engineering cookbook over 70 recipes for creating engineering and transforming features to build machine learning models |
| title_sub | over 70 recipes for creating, engineering, and transforming features to build machine learning models |
| topic | COMPUTERS / Data Processing COMPUTERS / Databases / Data Mining |
| topic_facet | COMPUTERS / Data Processing COMPUTERS / Databases / Data Mining |
| work_keys_str_mv | AT gallisoledad pythonfeatureengineeringcookbookover70recipesforcreatingengineeringandtransformingfeaturestobuildmachinelearningmodels |