عنوان
پدید آورنده
موضوع
رده
QA76.73.P98
QA76
.
73
.
P98
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
1782161414 (electronic bk.)
(Number (ISBN
9781782161417 (electronic bk.)
Erroneous ISBN
9781782161400
NATIONAL BIBLIOGRAPHY NUMBER
Number
b421640
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Building Machine Learning Systems with Python
General Material Designation
[Book]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (290 pages)
GENERAL NOTES
Text of Note
Description based upon print version of record
CONTENTS NOTE
Text of Note
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
Text of Note
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
Text of Note
Looking behind accuracy -- precision and recall
Text of Note
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
Text of Note
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
Text of Note
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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SUMMARY OR ABSTRACT
Text of Note
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
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Safari Books Online
Stock Number
CL0500000301
OTHER EDITION IN ANOTHER MEDIUM
Title
Building Machine Learning Systems with Python
International Standard Book Number
9781782161400
PIECE
Title
Safari books online
TOPICAL NAME USED AS SUBJECT
Machine learning.
Python (Computer program language)
Multimedia systems
Programming languages (Electronic computers)
Python (Computer program language)
(SUBJECT CATEGORY (Provisional
COM-- 000000
DEWEY DECIMAL CLASSIFICATION
Number
006
.
76
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA76
.
73
.
P98
PERSONAL NAME - PRIMARY RESPONSIBILITY
Richert, Willi.
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Coelho, Luis Pedro.
ORIGINATING SOURCE
Date of Transaction
20131101095755.0
Cataloguing Rules (Descriptive Conventions))
rda
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
