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Paper - Hands-on Scikit-Learn for machine learning applications: data science fundamentals with Python

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Paper Hands-on Scikit-Learn for machine learning applications: data science fundamentals with Python
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Hands-on Scikit-Learn for machine learning applications: data science fundamentals with Python: summary, description and annotation

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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 Youll 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 Who This Book Is For The aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.

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David Paper Hands-on Scikit-Learn for Machine Learning Applications Data - photo 1
David Paper
Hands-on Scikit-Learn for Machine Learning Applications
Data Science Fundamentals with Python
David Paper Logan UT USA Any source code or other supplementary material - photo 2
David Paper
Logan, UT, USA

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484253724 . For more detailed information, please visit http://www.apress.com/source-code .

ISBN 978-1-4842-5372-4 e-ISBN 978-1-4842-5373-1
https://doi.org/10.1007/978-1-4842-5373-1
David Paper 2020
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.

For my mother, brothers, and friends.

Introduction

We apply the popular Scikit-Learn library to demonstrate machine learning exercises with Python code to help readers solve machine learning problems. The book is designed for those with intermediate programming skills and some experience with machine learning algorithms. We focus on application of the algorithms rather than theory. So, readers should read about the theory online or from other sources if appropriate. The reader should also be willing to spend a lot of time working through the code examples because they are pretty deep. But, the effort will pay off because the examples are intended to help the reader tackle complex problems.

The book is organized into eight chapters. Chapterputs all knowledge together to review and present findings in a holistic manner.

Download this books example data by clicking the Download source code button found on the books catalog page at https://www.apress.com/us/book/9781484253724 .

Table of Contents
About the Author and About the Technical Reviewer
About the Author
David Paper
is a professor at Utah State University in the Management Information Systems - photo 3

is a professor at Utah State University in the Management Information Systems department. He is the author of two books Web Programming for Business: PHP Object-Oriented Programming with OracleandData Science Fundamentals for Python and MongoDB. He has over 70 publications in refereed journals such asOrganizational Research Methods,Communications of the ACM, Information & Management,Information Resource Management Journal,Communications of the AIS,Journal of Information Technology Case and Application Research, andLong Range Planning. He has also served on several editorial boards in various capacities, including associate editor. Besides growing up in family businesses, Dr. Paper has worked for Texas Instruments, DLS, Inc., and the Phoenix Small Business Administration. He has performed IS consulting work for IBM, AT&T, Octel, Utah Department of Transportation, and the Space Dynamics Laboratory. Dr. Papers teaching and research interests include data science, machine learning, process reengineering, object-oriented programming, and change management.

About the Technical Reviewer
Jojo Moolayil
is an artificial intelligence deep learning machine learning and decision - photo 4

is an artificial intelligence, deep learning, machine learning, and decision science professional and published author of three books:Smarter Decisions The Intersection of Internet of Things and Decision Science,Learn Keras for Deep Neural Networks, andApplied Supervised Learning with R. He has worked with industry leaders on several high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist AI.

Jojo was born and raised in Pune, India, and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the worlds largest pure-play analytics provider, and worked with the leaders of many Fortune 50 clients. He later worked with Flutura an IoT analytics start-up and GE, the pioneer and leader in Industrial AI.

He currently resides in Vancouver, BC. Apart from authoring books on deep learning, decision science, and IoT, Jojo has also been a technical reviewer for various books on the same subject with Apress and Packt publications. He is an active Data Science tutor and maintains a blog at http://blog.jojomoolayil.com .
  • Jojos personal web site: www.jojomoolayil.com

  • Business e-mail: mail@jojomoolayil.com

David Paper 2020
D. Paper Hands-on Scikit-Learn for Machine Learning Applications https://doi.org/10.1007/978-1-4842-5373-1_1
1. Introduction to Scikit-Learn
David Paper
(1)
Logan, UT, USA

Scikit-Learn is a Python library that provides simple and efficient tools for implementing supervised and unsupervised machine learning algorithms. The library is accessible to everyone because it is open source and commercially usable. It is built on NumPY, SciPy, and matplolib libraries, which means it is reliable, robust, and core to the Python language.

Scikit-Learn is focused on data modeling rather than data loading, cleansing, munging or manipulating. It is also very easy to use and relatively clean of programming bugs.

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