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|a eng
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|a 006.76
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|a Richert, Willi.
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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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|a Données textuelles
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|a Birmingham :
|b Packt Publishing,
|c 2013.
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|a Open Source Community experience distilled
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| 500 |
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|a Description d'après la consultation, 2016-09-13
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| 500 |
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|a Titre provenant de l'écran-titre
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|a La pagination de l'édition imprimée correspondante est de 290 p.
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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 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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|a Python (langage de programmation)
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| 650 |
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|a Apprentissage automatique
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| 650 |
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|a Machine learning
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| 650 |
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|a Python (Computer program language)
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| 700 |
1 |
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|a Coelho, Luis Pedro.
|4 aut
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| 776 |
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|t Richert, Willi. Building Machine Learning Systems with Python.
|c Birmingham : Packt Publishing, ©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
|z Accès à l'E-book
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| 993 |
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|a E-Book
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| 994 |
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|a BNUM
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| 995 |
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|a 195002970
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