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Building machine learning systems with Python : get more from your data through creating practical machine learning systems with Python /
This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing,
2015.
|
| Edición: | Second edition. |
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Coelho, Luis Pedro, |e author. | |
| 245 | 1 | 0 | |a Building machine learning systems with Python : |b get more from your data through creating practical machine learning systems with Python / |c Luis Pedro Coelho, Willi Richert. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, England : |b Packt Publishing, |c 2015. | |
| 264 | 4 | |c Ã2015 | |
| 300 | |a 1 online resource (326 pages) : |b illustrations. | ||
| 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 | ||
| 490 | 1 | |a Community experience distilled | |
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ebrary, viewed April 11, 2015). | |
| 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 -- a 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 nonexisting values; Comparing the runtime; Learning SciPy; Our first (tiny) application of machine learning | |
| 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: Classifying with Real-world Examples; The Iris dataset; Visualization is a good first step; 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; Classifying with scikit-learn; Looking at the decision boundaries; 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 a similar number of common words; Converting raw text into a bag of words; Counting words; Normalizing word count vectors; Removing less important words; Stemming | |
| 505 | 8 | |a Stop words on steroidsOur achievements and goals; Clustering; K-means; 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; Building a topic model; Comparing documents by topics; 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 kNN; Engineering the features; Training the classifier; Measuring the classifier's performance; Designing more features; Deciding how to improve; Bias-variance and their tradeoff; 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 post classification problem; Looking behind accuracy -- precision and recall | |
| 520 | |a This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 700 | 1 | |a Richert, Willi, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Coelho, Luis Pedro. |t Building machine learning systems with Python. |b Second edition |z 9781784392772 |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=971797 |z Texto completo |
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| 938 | |a YBP Library Services |b YANK |n 12358572 | ||
| 994 | |a 92 |b IZTAP | ||