Python machine learning: unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
2015
|
| Schriftenreihe: | Community experience distilled
|
| Schlagworte: | |
| Beschreibung: | Includes index Online resource; title from EPUB title page (OverDrive, January 04, 2016) |
| Beschreibung: | 1 online resource illustrations |
| ISBN: | 9781783555147 1783555149 1783555130 9781783555130 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV045351326 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 181210s2015 |||| o||u| ||||||eng d | ||
| 020 | |a 9781783555147 |9 978-1-78355-514-7 | ||
| 020 | |a 1783555149 |9 1-78355-514-9 | ||
| 020 | |a 1783555130 |9 1-78355-513-0 | ||
| 020 | |a 9781783555130 |9 978-1-78355-513-0 | ||
| 035 | |a (ZDB-4-ITC)ocn922532674 | ||
| 035 | |a (OCoLC)922532674 | ||
| 035 | |a (DE-599)BVBBV045351326 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 082 | 0 | |a 005.13/3 |2 23 | |
| 084 | |a ST 250 |0 (DE-625)143626: |2 rvk | ||
| 100 | 1 | |a Raschka, Sebastian |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Python machine learning |b unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics |c Sebastian Raschka ; foreword by Dr. Randal S. Olson |
| 264 | 1 | |a Birmingham, UK |b Packt Publishing |c 2015 | |
| 300 | |a 1 online resource |b illustrations | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Community experience distilled | |
| 500 | |a Includes index | ||
| 500 | |a Online resource; title from EPUB title page (OverDrive, January 04, 2016) | ||
| 505 | 8 | |a Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. | |
| 505 | 8 | |a What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. | |
| 505 | 8 | |a Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. | |
| 505 | 8 | |a Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models | |
| 650 | 7 | |a COMPUTERS / Programming Languages / Python |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 4 | |a Python (Computer program language) |a Machine learning | |
| 650 | 0 | 7 | |a Python 3.5 |0 (DE-588)1113598565 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Python 3.4 |0 (DE-588)1053433689 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Big Data |0 (DE-588)4802620-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Python |g Programmiersprache |0 (DE-588)4434275-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
| 689 | 0 | 2 | |a Big Data |0 (DE-588)4802620-7 |D s |
| 689 | 0 | 3 | |a Python 3.4 |0 (DE-588)1053433689 |D s |
| 689 | 0 | 4 | |a Python 3.5 |0 (DE-588)1113598565 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 689 | 1 | 0 | |a Python |g Programmiersprache |0 (DE-588)4434275-5 |D s |
| 689 | 1 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 1 | |8 2\p |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Raschka, Sebastian |t Python Machine Learning |d Birmingham : Packt Publishing Ltd, 2015 |z 9781783555130 |
| 912 | |a ZDB-4-ITC | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030737979 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804179176686616576 |
|---|---|
| any_adam_object | |
| author | Raschka, Sebastian |
| author_facet | Raschka, Sebastian |
| author_role | aut |
| author_sort | Raschka, Sebastian |
| author_variant | s r sr |
| building | Verbundindex |
| bvnumber | BV045351326 |
| classification_rvk | ST 250 |
| collection | ZDB-4-ITC |
| contents | Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models |
| ctrlnum | (ZDB-4-ITC)ocn922532674 (OCoLC)922532674 (DE-599)BVBBV045351326 |
| dewey-full | 005.13/3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.13/3 |
| dewey-search | 005.13/3 |
| dewey-sort | 15.13 13 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>05664nmm a2200673zc 4500</leader><controlfield tag="001">BV045351326</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">181210s2015 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781783555147</subfield><subfield code="9">978-1-78355-514-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1783555149</subfield><subfield code="9">1-78355-514-9</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1783555130</subfield><subfield code="9">1-78355-513-0</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781783555130</subfield><subfield code="9">978-1-78355-513-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-4-ITC)ocn922532674</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)922532674</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV045351326</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="082" ind1="0" ind2=" "><subfield code="a">005.13/3</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 250</subfield><subfield code="0">(DE-625)143626:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Raschka, Sebastian</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Python machine learning</subfield><subfield code="b">unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics</subfield><subfield code="c">Sebastian Raschka ; foreword by Dr. Randal S. Olson</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK</subfield><subfield code="b">Packt Publishing</subfield><subfield code="c">2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource</subfield><subfield code="b">illustrations</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="490" ind1="0" ind2=" "><subfield code="a">Community experience distilled</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes index</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Online resource; title from EPUB title page (OverDrive, January 04, 2016)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Programming Languages / Python</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Python (Computer program language)</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Computer program language)</subfield><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Python 3.5</subfield><subfield code="0">(DE-588)1113598565</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Python 3.4</subfield><subfield code="0">(DE-588)1053433689</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Big Data</subfield><subfield code="0">(DE-588)4802620-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Python</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4434275-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Big Data</subfield><subfield code="0">(DE-588)4802620-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Python 3.4</subfield><subfield code="0">(DE-588)1053433689</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="4"><subfield code="a">Python 3.5</subfield><subfield code="0">(DE-588)1113598565</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Python</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4434275-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Raschka, Sebastian</subfield><subfield code="t">Python Machine Learning</subfield><subfield code="d">Birmingham : Packt Publishing Ltd, 2015</subfield><subfield code="z">9781783555130</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-ITC</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030737979</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
| id | DE-604.BV045351326 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:15:42Z |
| institution | BVB |
| isbn | 9781783555147 1783555149 1783555130 9781783555130 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030737979 |
| oclc_num | 922532674 |
| open_access_boolean | |
| physical | 1 online resource illustrations |
| psigel | ZDB-4-ITC |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Packt Publishing |
| record_format | marc |
| series2 | Community experience distilled |
| spelling | Raschka, Sebastian Verfasser aut Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Sebastian Raschka ; foreword by Dr. Randal S. Olson Birmingham, UK Packt Publishing 2015 1 online resource illustrations txt rdacontent c rdamedia cr rdacarrier Community experience distilled Includes index Online resource; title from EPUB title page (OverDrive, January 04, 2016) Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models COMPUTERS / Programming Languages / Python bisacsh Machine learning fast Python (Computer program language) fast Python (Computer program language) Machine learning Python 3.5 (DE-588)1113598565 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Python 3.4 (DE-588)1053433689 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Datenanalyse (DE-588)4123037-1 s Big Data (DE-588)4802620-7 s Python 3.4 (DE-588)1053433689 s Python 3.5 (DE-588)1113598565 s 1\p DE-604 Python Programmiersprache (DE-588)4434275-5 s 2\p DE-604 Erscheint auch als Druck-Ausgabe Raschka, Sebastian Python Machine Learning Birmingham : Packt Publishing Ltd, 2015 9781783555130 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Raschka, Sebastian Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models COMPUTERS / Programming Languages / Python bisacsh Machine learning fast Python (Computer program language) fast Python (Computer program language) Machine learning Python 3.5 (DE-588)1113598565 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python 3.4 (DE-588)1053433689 gnd Big Data (DE-588)4802620-7 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)1113598565 (DE-588)4193754-5 (DE-588)4123037-1 (DE-588)1053433689 (DE-588)4802620-7 (DE-588)4434275-5 |
| title | Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics |
| title_auth | Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics |
| title_exact_search | Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics |
| title_full | Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Sebastian Raschka ; foreword by Dr. Randal S. Olson |
| title_fullStr | Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Sebastian Raschka ; foreword by Dr. Randal S. Olson |
| title_full_unstemmed | Python machine learning unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Sebastian Raschka ; foreword by Dr. Randal S. Olson |
| title_short | Python machine learning |
| title_sort | python machine learning unlock deeper insights into machine learning with this vital guide to cutting edge predictive analytics |
| title_sub | unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics |
| topic | COMPUTERS / Programming Languages / Python bisacsh Machine learning fast Python (Computer program language) fast Python (Computer program language) Machine learning Python 3.5 (DE-588)1113598565 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python 3.4 (DE-588)1053433689 gnd Big Data (DE-588)4802620-7 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | COMPUTERS / Programming Languages / Python Machine learning Python (Computer program language) Python (Computer program language) Machine learning Python 3.5 Maschinelles Lernen Datenanalyse Python 3.4 Big Data Python Programmiersprache |
| work_keys_str_mv | AT raschkasebastian pythonmachinelearningunlockdeeperinsightsintomachinelearningwiththisvitalguidetocuttingedgepredictiveanalytics |