(Ebook) Hands-on Scikit-Learn for machine learning applications: data science fundamentals with Python by David Paper ISBN 9780933333338, 9781484253724, 9781484253731, 9789109027774, 0933333331, 1484253728, 1484253736, 9109027777

(Ebook) Hands-on Scikit-Learn for machine learning applications: data science fundamentals with Python by David Paper ISBN 9780933333338, 9781484253724, 9781484253731, 9789109027774, 0933333331, 1484253728, 1484253736, 9109027777

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
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.