Verfügbarkeit: Introduction to machine learning with Python

Gespeichert in:

Bibliographische Detailangaben
Titel:	Introduction to machine learning with Python a guide for data scientists
Von:	Andreas C. Müller and Sarah Guido
Person:	Müller, Andreas Christian aut Guido, Sarah
Hauptverfassende:	Müller, Andreas Christian (VerfasserIn), Guido, Sarah (VerfasserIn)
Entstanden:	Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly [October 2016]
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	© 2016
Ausgabe:	First edition
Schlagworte:	Maschinelles Lernen Python > Programmiersprache
Online-Zugang:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713402&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Hier auch später erschienene, unveränderte Nachdrucke (2017)
Beschreibung:	xii, 378 Seiten Illustrationen
ISBN:	9781449369415 1449369413

Internformat

MARC


LEADER	00000nam a2200000 c 4500
001	BV043292304
003	DE-604
005	20181119
007	t\|
008	160119s2016 xx a\|\|\| \|\|\|\| 00\|\|\| eng d
020			\|a 9781449369415 \|9 978-1-449-36941-5
020			\|a 1449369413 \|9 1-449-36941-3
035			\|a (OCoLC)964453057
035			\|a (DE-599)BVBBV043292304
040			\|a DE-604 \|b ger \|e rda
041	0		\|a eng
049			\|a DE-739 \|a DE-862 \|a DE-1051 \|a DE-19 \|a DE-573 \|a DE-Aug4 \|a DE-11 \|a DE-523 \|a DE-91G \|a DE-91 \|a DE-83 \|a DE-188 \|a DE-521 \|a DE-861 \|a DE-M382 \|a DE-29T \|a DE-355
084			\|a ST 250 \|0 (DE-625)143626: \|2 rvk
084			\|a ST 300 \|0 (DE-625)143650: \|2 rvk
084			\|a 68P01 \|2 msc
084			\|a DAT 708f \|2 stub
084			\|a DAT 366f \|2 stub
100	1		\|a Müller, Andreas Christian \|0 (DE-588)1060129469 \|4 aut
245	1	0	\|a Introduction to machine learning with Python \|b a guide for data scientists \|c Andreas C. Müller and Sarah Guido
250			\|a First edition
264		0	\|a Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo \|b O'Reilly \|c [October 2016]
264		4	\|c © 2016
300			\|a xii, 378 Seiten \|b Illustrationen
336			\|b txt \|2 rdacontent
337			\|b n \|2 rdamedia
338			\|b nc \|2 rdacarrier
500			\|a Hier auch später erschienene, unveränderte Nachdrucke (2017)
650	0	7	\|a Maschinelles Lernen \|0 (DE-588)4193754-5 \|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 Python \|g Programmiersprache \|0 (DE-588)4434275-5 \|D s
689	0	1	\|a Maschinelles Lernen \|0 (DE-588)4193754-5 \|D s
689	0		\|5 DE-604
700	1		\|a Guido, Sarah \|0 (DE-588)1117052265 \|4 aut
776	0	8	\|i Erscheint auch als \|n Online-Ausgabe \|z 978-1-449-36990-3
856	4	2	\|m Digitalisierung UB Passau - ADAM Catalogue Enrichment \|q application/pdf \|u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713402&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA \|3 Inhaltsverzeichnis
943	1		\|a oai:aleph.bib-bvb.de:BVB01-028713402

Datensatz im Suchindex

DE-BY-TUM_call_number	0003 DAT 708f 2017 L 601 0102 DAT 708f 2017 A 4019 0303 DAT 708f 2017 L 640
DE-BY-TUM_katkey	2279055
DE-BY-TUM_location	00 01 03
DE-BY-TUM_media_number	040008641952 040008641996 040008641985 040008642022 040008641941 040008642033 040008641963 040008642011 040008641930 040008641974 040008642102 040008642000 040008259221 040008137144 040008137097 040008137155 040008137086 040008137100 040008137122 040008137111 040008137075 040008137064 040008137133
DE-BY-UBR_call_number	440/ST 300 M946 I6
DE-BY-UBR_katkey	6399076
DE-BY-UBR_location	UB Handapparat Recht/Wirtschaft Prof. Schwarz
DE-BY-UBR_media_number	TEMP12713595
_version_	1835101084615442433
adam_text	Table of Contents Preface...................................................... --- ..............vii 1. Introduction.................................................. ..............1 Why Machine Learning? 1 Problems Machine Learning Can Solve 2 Knowing Your Task and Knowing Your Data 4 Why Python? 5 scikit-learn 5 Installing scikit-learn 6 Essential Libraries and Tools 7 Jupyter Notebook 7 NumPy 7 SciPy 8 matplotlib 9 pandas 10 mglearn 11 Python 2 Versus Python 3 12 Versions Used in this Book 12 A First Application: Classifying Iris Species 13 Meet the Data 14 Measuring Success: Training and Testing Data 17 First Things First: Look at Your Data 19 Building Your First Model: k-Nearest Neighbors 20 Making Predictions 22 Evaluating the Model 22 Summary and Outlook 23 jii 2. Supervised Learning.................................................... 25 Classification and Regression 25 Generalization, Overfitting, and Underfitting 26 Relation of Model Complexity to Dataset Size 29 Supervised Machine Learning Algorithms 29 Some Sample Datasets 30 k-Nearest Neighbors 35 Linear Models 45 Naive Bayes Classifiers 68 Decision Trees 70 Ensembles of Decision Trees 83 Kernelized Support Vector Machines 92 Neural Networks (Deep Learning) 104 Uncertainty Estimates from Classifiers 119 The Decision Function 120 Predicting Probabilities 122 Uncertainty in Multiclass Classification 124 Summary and Outlook 127 3. Unsupervised Learning and Preprocessing................................. 131 Types of Unsupervised Learning 131 Challenges in Unsupervised Learning 132 Preprocessing and Scaling 132 Different Kinds of Preprocessing 133 Applying Data Transformations 134 Scaling Training and Test Data the Same Way 136 The Effect of Preprocessing on Supervised Learning 138 Dimensionality Reduction, Feature Extraction, and Manifold Learning 140 Principal Component Analysis (PCA) 140 Non-Negative Matrix Factorization (NMF) 156 Manifold Learning with t-SNE 163 Clustering 168 k-Means Clustering 168 Agglomerative Clustering 182 DBSCAN 187 Comparing and Evaluating Clustering Algorithms 191 Summary of Clustering Methods 207 Summary and Outlook 208 4. Representing Data and Engineering Features..............................211 Categorical Variables 212 One-Hot-Encoding (Dummy Variables) 2.13 iv \| Table of Contents Numbers Can Encode Categoricals 218 Binning, Discretization, Linear Models, and Trees 220 Interactions and Polynomials 224 Univariate Nonlinear Transformations 232 Automatic Feature Selection 236 Univariate Statistics 236 Model-Based Feature Selection 238 Iterative Feature Selection 240 Utilizing Expert Knowledge 242 Summary and Outlook 250 5. Model Evaluation and Improvement.......................................... 251 Cross-Validation 252 Cross-Validation in scikit-learn 253 Benefits of Cross-Validation 254 Stratified k-Fold Cross-Validation and Other Strategies 254 Grid Search 260 Simple Grid Search 261 The Danger of Overfitting the Parameters and the Validation Set 261 Grid Search with Cross-Validation 263 Evaluation Metrics and Scoring 275 Keep the End Goal in Mind 275 Metrics for Binary Classification 276 Metrics for Multiclass Classification 296 Regression Metrics 299 Using Evaluation Metrics in Model Selection 300 Summary and Outlook 302 6. Algorithm Chains and Pipelines......................................... 305 Parameter Selection with Preprocessing 306 Building Pipelines 308 Using Pipelines in Grid Searches 309 The General Pipeline Interface 312 Convenient Pipeline Creation with make_pipeline 313 Accessing Step Attributes 314 Accessing Attributes in a Grid-Searched Pipeline 315 Grid-Searching Preprocessing Steps and Model Parameters 317 Grid-Searching Which Model To Use 319 Summary and Outlook 320 7. Working with Text Data.., 000000«00900 Ï00003000003009JQ OOOOOO Types of Data Represented as Strings 323 Table of Contents \| v Example Application: Sentiment Analysis of Movie Reviews 325 Representing Text Data as a Bag of Words 327 Applying Bag-oT Words to a Toy Dataset 329 Bag-of-Words for Movie Reviews 330 Stopwords 334 Rescaling the Data with tf—idf 336 Investigating Model Coefficients 338 Bag-of-Words with More Than One Word (n-Grams) 339 Advanced Tokenization, Stemming, and Lemmatization 344 Topic Modeling and Document Clustering 347 Latent Dirichlet Allocation 348 Summary and Outlook 355 8. Wrapping Up.......................................................... 357 Approaching a Machine Learning Problem 357 Humans in the Loop 358 From Prototype to Production 359 Testing Production Systems 359 Building Your Own Estimator 360 Where to Go from Here 361 Theory 361 Other Machine Learning Frameworks and Packages 362 Ranking, Recommender Systems, and Other Kinds of Learning 363 Probabilistic Modeling, Inference, and Probabilistic Programming 363 Neural Networks 364 Scaling to Larger Datasets 364 Honing Your Skills 365 Conclusion 366 Index................................................................... 367 vi j Table of Contents
any_adam_object	1
author	Müller, Andreas Christian Guido, Sarah
author_GND	(DE-588)1060129469 (DE-588)1117052265
author_facet	Müller, Andreas Christian Guido, Sarah
author_role	aut aut
author_sort	Müller, Andreas Christian
author_variant	a c m ac acm s g sg
building	Verbundindex
bvnumber	BV043292304
classification_rvk	ST 250 ST 300
classification_tum	DAT 708f DAT 366f
ctrlnum	(OCoLC)964453057 (DE-599)BVBBV043292304
discipline	Informatik
edition	First edition
format	Book
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02018nam a2200457 c 4500</leader><controlfield tag="001">BV043292304</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20181119 </controlfield><controlfield tag="007">t\|</controlfield><controlfield tag="008">160119s2016 xx a\|\|\| \|\|\|\| 00\|\|\| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781449369415</subfield><subfield code="9">978-1-449-36941-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1449369413</subfield><subfield code="9">1-449-36941-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)964453057</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV043292304</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-739</subfield><subfield code="a">DE-862</subfield><subfield code="a">DE-1051</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-Aug4</subfield><subfield code="a">DE-11</subfield><subfield code="a">DE-523</subfield><subfield code="a">DE-91G</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-83</subfield><subfield code="a">DE-188</subfield><subfield code="a">DE-521</subfield><subfield code="a">DE-861</subfield><subfield code="a">DE-M382</subfield><subfield code="a">DE-29T</subfield><subfield code="a">DE-355</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="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">68P01</subfield><subfield code="2">msc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 708f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 366f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Müller, Andreas Christian</subfield><subfield code="0">(DE-588)1060129469</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Introduction to machine learning with Python</subfield><subfield code="b">a guide for data scientists</subfield><subfield code="c">Andreas C. Müller and Sarah Guido</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="0"><subfield code="a">Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo</subfield><subfield code="b">O'Reilly</subfield><subfield code="c">[October 2016]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2016</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xii, 378 Seiten</subfield><subfield code="b">Illustrationen</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">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Hier auch später erschienene, unveränderte Nachdrucke (2017)</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">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">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="0" 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="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guido, Sarah</subfield><subfield code="0">(DE-588)1117052265</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-449-36990-3</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713402&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-028713402</subfield></datafield></record></collection>
id	DE-604.BV043292304
illustrated	Illustrated
indexdate	2024-12-20T17:33:01Z
institution	BVB
isbn	9781449369415 1449369413
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-028713402
oclc_num	964453057
open_access_boolean
owner	DE-739 DE-862 DE-BY-FWS DE-1051 DE-19 DE-BY-UBM DE-573 DE-Aug4 DE-11 DE-523 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-83 DE-188 DE-521 DE-861 DE-M382 DE-29T DE-355 DE-BY-UBR
owner_facet	DE-739 DE-862 DE-BY-FWS DE-1051 DE-19 DE-BY-UBM DE-573 DE-Aug4 DE-11 DE-523 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-83 DE-188 DE-521 DE-861 DE-M382 DE-29T DE-355 DE-BY-UBR
physical	xii, 378 Seiten Illustrationen
publishDate	2016
publishDateSearch	2016
publishDateSort	2016
record_format	marc
spellingShingle	Müller, Andreas Christian Guido, Sarah Introduction to machine learning with Python a guide for data scientists Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd
subject_GND	(DE-588)4193754-5 (DE-588)4434275-5
title	Introduction to machine learning with Python a guide for data scientists
title_auth	Introduction to machine learning with Python a guide for data scientists
title_exact_search	Introduction to machine learning with Python a guide for data scientists
title_full	Introduction to machine learning with Python a guide for data scientists Andreas C. Müller and Sarah Guido
title_fullStr	Introduction to machine learning with Python a guide for data scientists Andreas C. Müller and Sarah Guido
title_full_unstemmed	Introduction to machine learning with Python a guide for data scientists Andreas C. Müller and Sarah Guido
title_short	Introduction to machine learning with Python
title_sort	introduction to machine learning with python a guide for data scientists
title_sub	a guide for data scientists
topic	Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd
topic_facet	Maschinelles Lernen Python Programmiersprache
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713402&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT mullerandreaschristian introductiontomachinelearningwithpythonaguidefordatascientists AT guidosarah introductiontomachinelearningwithpythonaguidefordatascientists

Verfügbarkeit

‌

Per Ortsleihe bestellen Per Ortsleihe bestellen Inhaltsverzeichnis

An UBR ausleihen
Ausleihen an der UBR sind mit eigenem Ausweis möglich.