東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Hands-on Scikit-Learn for machine le...

Paper, David.

Linked to FindBook

Google Book

Amazon

博客來

Hands-on Scikit-Learn for machine learning applications = data science fundamentals with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hands-on Scikit-Learn for machine learning applications/ by David Paper.
Reminder of title:	data science fundamentals with Python /
Author:	Paper, David.
Published:	Berkeley, CA :Apress : : 2020.,
Description:	xiii, 242 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction to Scikit-Learn -- 2. Classification from Simple Training Sets -- 3. Classification from Complex Training Sets -- 4. Predictive Modeling through Regression -- 5. Scikit-Learn Classifier Tuning from Simple Training Sets -- 6. Scikit-Learn Classifier Tuning from Complex Training Sets -- 7. Scikit-Learn RegressionTuning -- 8. Putting it All Together.
Contained By:	Springer eBooks
Subject:	Python (Computer program language) -
Online resource:	https://doi.org/10.1007/978-1-4842-5373-1
ISBN:	9781484253731

Hands-on Scikit-Learn for machine learning applications = data science fundamentals with Python /
Paper, David.

Hands-on Scikit-Learn for machine learning applicationsdata science fundamentals with Python /[electronic resource] :by David Paper. - Berkeley, CA :Apress :2020. - xiii, 242 p. :ill., digital ;24 cm.

1. Introduction to Scikit-Learn -- 2. Classification from Simple Training Sets -- 3. Classification from Complex Training Sets -- 4. Predictive Modeling through Regression -- 5. Scikit-Learn Classifier Tuning from Simple Training Sets -- 6. Scikit-Learn Classifier Tuning from Complex Training Sets -- 7. Scikit-Learn RegressionTuning -- 8. Putting it All Together.

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate data into vectors and matrices for algorithmic processing Become familiar with the Anaconda distribution used in data science Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction Tune algorithms and find the best algorithms for each dataset Load data from and save to CSV, JSON, Numpy, and Pandas formats.

ISBN: 9781484253731

Standard No.: 10.1007/978-1-4842-5373-1doiSubjects--Topical Terms:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98 / P374 2020

Dewey Class. No.: 005.133

Hands-on Scikit-Learn for machine learning applications = data science fundamentals with Python /
LDR:03303nmm a2200325 a 4500 001 2214899
003 DE-He213
005 20200324102827.0
006 m d
007 cr nn 008maaau
008 201118s2020 cau s 0 eng d
020 $a 9781484253731 $q (electronic bk.)
020 $a 9781484253724 $q (paper)
024 7 $a 10.1007/978-1-4842-5373-1 $2 doi
035 $a 978-1-4842-5373-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98 $b P374 2020
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b P214 2020
100 1 $a Paper, David. $3 815332
245 1 0 $a Hands-on Scikit-Learn for machine learning applications $h [electronic resource] : $b data science fundamentals with Python / $c by David Paper.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2020.
300 $a xiii, 242 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction to Scikit-Learn -- 2. Classification from Simple Training Sets -- 3. Classification from Complex Training Sets -- 4. Predictive Modeling through Regression -- 5. Scikit-Learn Classifier Tuning from Simple Training Sets -- 6. Scikit-Learn Classifier Tuning from Complex Training Sets -- 7. Scikit-Learn RegressionTuning -- 8. Putting it All Together.
520 $a Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate data into vectors and matrices for algorithmic processing Become familiar with the Anaconda distribution used in data science Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction Tune algorithms and find the best algorithms for each dataset Load data from and save to CSV, JSON, Numpy, and Pandas formats.
650 0 $a Python (Computer program language) $3 729789
650 0 $a Machine learning. $3 533906
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Python. $3 3201289
650 2 4 $a Big Data. $3 3134868
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-5373-1
950 $a Professional and Applied Computing (Springer-12059)