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Hands-on Scikit-Learn for Machine Learning Applications de David Paper

Hands-on Scikit-Learn for Machine Learning Applications

Autor David Paper
en Limba Engleză Paperback – 18 noi 2019
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 complexmachine 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

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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Specificații

ISBN-13: 9781484253724
ISBN-10: 1484253728
Pagini: 256
Ilustrații: XIII, 242 p. 33 illus.
Dimensiuni: 178 x 254 x 15 mm
Greutate: 0.49 kg
Ediția:First Edition
Editura: Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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.

Notă biografică

¿Dr. Paper is a retired academic from the Utah State University (USU) Data Analytics and Management Information Systems department in the Huntsman School of Business. He has over 30 years of higher education teaching experience. At USU, he taught for 27 years in the classroom and distance education over satellite. He taught a variety of classes at the undergraduate, graduate, and doctorate levels, but he specializes in applied technology education.
Dr. Paper has competency in several programming languages, but his focus is currently on deep learning with Python in the TensorFlow-Colab Ecosystem. He has published extensively on machine learning, including Apress books: Data Science Fundamentals for Python and MongoDB,¿Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, and¿TensorFlow 2.x in the Colaboratory Cloud: An Introduction to Deep Learning on Google's Cloud Service. He has also published more than 100 academic articles.
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, the Utah Department of Transportation, and the Space Dynamics Laboratory. He has worked on research projects with several corporations, including Caterpillar, Fannie Mae, Comdisco, IBM, RayChem, Ralston Purina, and Monsanto. He maintains contacts in corporations such as Google, Micron, Oracle, and Goldman Sachs.

Textul de pe ultima copertă

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


Caracteristici

Introduces the popular Scikit-Learn library for machine learning algorithms in Python Provides examples in Python that are made specifically for data science Teaches principles of machine learning that are needed for success in the field