Paper Hands-on Scikit-Learn for Machine Learning Applications

Data Science Fundamentals with Python
1. ed
ISBN: 978-1-4842-5373-1
Verlag: Apress
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 247 Seiten

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 LearnWork 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 scienceApply 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.



Dr. David Paper is a professor at Utah State University in the Management Information Systems department. He wrote the book Web Programming for Business: PHP Object-Oriented Programming with Oracle and he has over 70 publications in refereed journals such as Organizational Research Methods, Communications of the ACM, Information & Management, Information Resource Management Journal, Communications of the AIS, Journal of Information Technology Case and Application Research, and Long 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. Paper's teaching and research interests include data science, process reengineering, object-oriented programming, electronic customer relationship management, change management, e-commerce, and enterprise integration.

Autoren/Hrsg.

Paper, David

Inhaltsverzeichnis

1;Table of Contents;5
2;About the Author;8
3;About the Technical Reviewer;9
4;Introduction;10
5;Chapter 1: Introduction to Scikit-Learn;11
5.1;Machine Learning;11
5.2;Anaconda;12
5.3;Scikit-Learn;13
5.4;Data Sets;13
5.5;Characterize Data;14
5.5.1;Simple Classification Data;14
5.5.1.1;Iris Data;14
5.5.1.2;Wine Data;17
5.5.1.3;Bank Data;19
5.5.1.4;Digits Data;20
5.5.2;Complex Classification Data;24
5.5.2.1;Newsgroup Data;25
5.5.2.2;MNIST Data;26
5.5.2.3;Faces Data;29
5.5.3;Regression Data;31
5.5.3.1;Tips Data;31
5.5.3.2;Red and White Wine;33
5.5.3.3;Boston Data;36
5.6;Feature Scaling;37
5.7;Dimensionality Reduction;40
6;Chapter 2: Classification from Simple Training Sets;46
6.1;Simple Data Sets;47
6.1.1;Classifying Wine Data;47
6.1.2;Classifying Digits;52
6.1.3;Classifying Bank Data;61
6.1.4;Classifying make_moons;73
7;Chapter 3: Classification from  Complex Training Sets;79
7.1;Complex Data Sets;79
7.1.1;Classifying fetch_20newsgroups;79
7.1.2;Classifying MNIST;87
7.1.2.1;Training with the Entire MNIST Data Set;87
7.1.2.2;Training MNIST Sample Data;95
7.1.3;Classifying fetch_lfw_people;103
8;Chapter 4: Predictive Modeling Through Regression;113
8.1;Regression Data Sets;113
8.2;Regressing tips;114
8.3;Regressing boston;125
8.4;Regressing wine data;130
9;Chapter 5: Scikit-Learn Classifier Tuning from Simple Training Sets;145
9.1;Tuning Data Sets;147
9.2;Tuning Iris Data;148
9.3;Tuning Digits Data;152
9.4;Tuning Bank Data;157
9.5;Tuning Wine Data;165
10;Chapter 6: Scikit-Learn Classifier Tuning from Complex Training Sets;172
10.1;Tuning Data Sets;173
10.2;Tuning fetch_1fw_people;173
10.3;Tuning MNIST;182
10.4;Tuning fetch_20newsgroups;191
11;Chapter 7: Scikit-Learn Regression Tuning;196
11.1;Tuning Data Sets;197
11.2;Tuning tips;197
11.3;Tuning boston;206
11.4;Tuning wine;215
12;Chapter 8: Putting It All Together;221
12.1;The Journey;221
12.2;Value and Cost;222
12.3;MNIST Value and Cost;224
12.3.1;Explaining MNIST to Money People;228
12.3.2;Explaining Output to Money People;228
12.3.3;Explaining the Confusion Matrix to Money People;229
12.3.4;Explaining Visualizations to Money People;230
12.3.5;Value and Cost;230
12.4;fetch_lfw_people Value and Cost;231
12.4.1;Explaining fetch_lfw_people to Money People;235
12.4.2;Explaining Output to Money People;235
12.4.3;Explaining Visualizations to Money People;236
12.4.4;Value and Cost;236
12.5;fetch_20newsgroups Value and Cost;237
12.5.1;Explaining fetch_20newsgroups to Money People;241
12.5.2;Explaining Output to Money People;241
12.5.3;Explaining the Confusion Matrix to Money People;241
12.5.4;Value and Cost;242
13;Index;244