Building Machine Learning Systems with Python.

Mostrar otras versiones (2)

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Richert, Willi
Otros Autores: Coelho, Luis Pedro
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2013.
Temas:
Machine learning.
Python (Computer program language)
Apprentissage automatique.
Python (Langage de programmation)
COMPUTERS > General.
Machine learning
Llibres electrònics.
Acceso en línea:Texto completo

MARC

LEADER 00000cam a2200000Mu 4500
001 EBOOKCENTRAL_ocn854974334
003 OCoLC
005 20240329122006.0
006 m o d
007 cr cnu---unuuu
008 130803s2013 enk o 000 0 eng d
040 |a EBLCP  |b eng  |e pn  |c EBLCP  |d IDEBK  |d YDXCP  |d UMI  |d N$T  |d DEBSZ  |d RIV  |d OCLCQ  |d OCLCO  |d OCLCF  |d E7B  |d FHM  |d COO  |d OCLCQ  |d D6H  |d FEM  |d JBG  |d AGLDB  |d MOR  |d CCO  |d PIFAG  |d ZCU  |d MERUC  |d OCLCQ  |d U3W  |d STF  |d WRM  |d VTS  |d CEF  |d NRAMU  |d ICG  |d INT  |d VT2  |d AU@  |d OCLCQ  |d UAB  |d A6Q  |d DKC  |d OCLCQ  |d M8D  |d OCLCQ  |d OCLCO  |d QGK  |d OCLCQ  |d OCLCO  |d OCLCL 
019 |a 857066405  |a 859144341  |a 968003955  |a 988416578  |a 992040890  |a 1037774721  |a 1038661306  |a 1045496503  |a 1055350653  |a 1058458126  |a 1081214322  |a 1083600885  |a 1103251293  |a 1129354440  |a 1153054164  |a 1259201951  |a 1264902110  |a 1297255154  |a 1297579663 
020 |a 9781782161417  |q (electronic bk.) 
020 |a 1782161414  |q (electronic bk.) 
020 |a 1782161406 
020 |a 9781782161400 
020 |z 9781782161400 
029 1 |a AU@  |b 000052162168 
029 1 |a AU@  |b 000062476333 
029 1 |a DEBBG  |b BV041432668 
029 1 |a DEBBG  |b BV043776882 
029 1 |a DEBBG  |b BV044189322 
029 1 |a DEBSZ  |b 39757813X 
029 1 |a DEBSZ  |b 398285934 
029 1 |a DEBSZ  |b 472794671 
029 1 |a GBVCP  |b 785372598 
029 1 |a NZ1  |b 15908238 
029 1 |a ZWZ  |b 195002970 
029 1 |a AU@  |b 000066763245 
035 |a (OCoLC)854974334  |z (OCoLC)857066405  |z (OCoLC)859144341  |z (OCoLC)968003955  |z (OCoLC)988416578  |z (OCoLC)992040890  |z (OCoLC)1037774721  |z (OCoLC)1038661306  |z (OCoLC)1045496503  |z (OCoLC)1055350653  |z (OCoLC)1058458126  |z (OCoLC)1081214322  |z (OCoLC)1083600885  |z (OCoLC)1103251293  |z (OCoLC)1129354440  |z (OCoLC)1153054164  |z (OCoLC)1259201951  |z (OCoLC)1264902110  |z (OCoLC)1297255154  |z (OCoLC)1297579663 
037 |a CL0500000301  |b Safari Books Online 
050 4 |a QA76.73.P98 
072 7 |a COM  |x 000000  |2 bisacsh 
082 0 4 |a 006.76 
049 |a UAMI 
100 1 |a Richert, Willi. 
245 1 0 |a Building Machine Learning Systems with Python. 
260 |a Birmingham :  |b Packt Publishing,  |c 2013. 
300 |a 1 online resource (290 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |2 rda 
588 0 |a Print version record. 
505 0 |a Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Python Machine Learning; Machine learning and Python -- the dream team; What the book will teach you (and what it will not); What to do when you are stuck; Getting started; Introduction to NumPy, SciPy, and Matplotlib; Installing Python; Chewing data efficiently with NumPy and intelligently with SciPy; Learning NumPy; Indexing; Handling non-existing values; Comparing runtime behaviors; Learning SciPy; Our first (tiny) machine learning application. 
505 8 |a Reading in the dataPreprocessing and cleaning the data; Choosing the right model and learning algorithm; Before building our first model; Starting with a simple straight line; Towards some advanced stuff; Stepping back to go forward -- another look at our data; Training and testing; Answering our initial question; Summary; Chapter 2: Learning How to Classify with Real-world Examples; The Iris dataset; The first step is visualization; Building our first classification model; Evaluation -- holding out data and cross-validation; Building more complex classifiers. 
505 8 |a A more complex dataset and a more complex classifierLearning about the Seeds dataset; Features and feature engineering; Nearest neighbor classification; Binary and multiclass classification; Summary; Chapter 3: Clustering -- Finding Related Posts; Measuring the relatedness of posts; How not to do it; How to do it; Preprocessing -- similarity measured as similar number of common words; Converting raw text into a bag-of-words; Counting words; Normalizing the word count vectors; Removing less important words; Stemming; Installing and using NLTK; Extending the vectorizer with NLTK's stemmer. 
505 8 |a Stop words on steroidsOur achievements and goals; Clustering; KMeans; Getting test data to evaluate our ideas on; Clustering posts; Solving our initial challenge; Another look at noise; Tweaking the parameters; Summary; Chapter 4: Topic Modeling; Latent Dirichlet allocation (LDA); Building a topic model; Comparing similarity in topic space; Modeling the whole of Wikipedia; Choosing the number of topics; Summary; Chapter 5: Classification -- Detecting Poor Answers; Sketching our roadmap; Learning to classify classy answers; Tuning the instance; Tuning the classifier; Fetching the data. 
505 8 |a Slimming the data down to chewable chunksPreselection and processing of attributes; Defining what is a good answer; Creating our first classifier; Starting with the k-nearest neighbor (kNN) algorithm; Engineering the features; Training the classifier; Measuring the classifier's performance; Designing more features; Deciding how to improve; Bias-variance and its trade-off; Fixing high bias; Fixing high variance; High bias or low bias; Using logistic regression; A bit of math with a small example; Applying logistic regression to our postclassification problem. 
505 8 |a Looking behind accuracy -- precision and recall. 
520 |a This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them. This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro. 
546 |a English. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
650 0 |a Machine learning. 
650 0 |a Python (Computer program language) 
650 6 |a Apprentissage automatique. 
650 6 |a Python (Langage de programmation) 
650 7 |a COMPUTERS  |x General.  |2 bisacsh 
650 7 |a Machine learning  |2 fast 
650 7 |a Python (Computer program language)  |2 fast 
655 4 |a Llibres electrònics. 
700 1 |a Coelho, Luis Pedro. 
758 |i has work:  |a Building Machine Learning Systems with Python (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCFytHfwxMpRd9gbPdk8Cry  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Richert, Willi.  |t Building Machine Learning Systems with Python.  |d Birmingham : Packt Publishing, ©2013  |z 9781782161400 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1236045  |z Texto completo 
938 |a EBL - Ebook Library  |b EBLB  |n EBL1236045 
938 |a ebrary  |b EBRY  |n ebr10742638 
938 |a EBSCOhost  |b EBSC  |n 619996 
938 |a ProQuest MyiLibrary Digital eBook Collection  |b IDEB  |n cis26009285 
938 |a YBP Library Services  |b YANK  |n 10906867 
994 |a 92  |b IZTAP 

Ejemplares similares