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|a ABES
|b eng
|e AFNOR
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|a eng
|2 639-2
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|a QA76.73.P98
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|a 006.76
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1 |
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|0 (IdRef)195002717
|1 http://www.idref.fr/195002717/id
|a Richert, Willi.
|4 aut.
|e Auteur
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| 245 |
1 |
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|a Building Machine Learning Systems with Python :
|b master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide
|c Willi Richert, Luis Pedro Coelho.
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| 256 |
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|a Données textuelles.
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| 264 |
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1 |
|a Birmingham :
|b Packt Publishing,
|c 2013.
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| 336 |
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|b txt
|2 rdacontent
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| 337 |
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|b c
|2 rdamedia
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|2 isbdmedia
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|b ceb
|2 RDAfrCarrier
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|a Open Source Community experience distilled
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| 500 |
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|a Titre provenant de l'écran-titre.
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| 500 |
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|a La pagination de l'édition imprimée correspondante est de 290 p.
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| 501 |
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|a Description d'après la consultation, 2016-09-13.
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0 |
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|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 -- 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 -- Looking behind accuracy -- precision and recall -- 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 -- 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 -- 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
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|a L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition
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|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
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| 538 |
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|a Nécessite un navigateur Internet ; un lecteur de fichier PDF.
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| 650 |
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7 |
|0 (IdRef)051626225
|1 http://www.idref.fr/051626225/id
|a Python (langage de programmation).
|2 ram
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| 650 |
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7 |
|0 (IdRef)027940373
|1 http://www.idref.fr/027940373/id
|a Apprentissage automatique.
|2 ram
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| 650 |
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|a Machine learning.
|2 lc
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| 650 |
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|a Python (Computer program language).
|2 lc
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| 700 |
1 |
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|0 (IdRef)195002946
|1 http://www.idref.fr/195002946/id
|a Coelho, Luis Pedro.
|4 aut.
|e Auteur
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| 760 |
0 |
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|t Open source* : community experience distilled
|d Birmingham : Packt publishing, 2013
|w (ABES)172618363
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| 830 |
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|a Open source*.
|f 2013
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| 856 |
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|q HTML
|u https://srvext.uco.fr/login?url=https://univ.scholarvox.com/book/88850214
|w Données éditeur
|z Accès à l'E-book
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|a i#
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