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

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

9781484253731 (electronic book)
1484253736 (electronic book)
9781484253724
1484253728

https://doi.org/10.1007/978-1-4842-5373-1
https://doi.org/10.1007/978-1-4842-5

Berkeley, CA : Apress, 2020.

English

1 online resource (xiii, 242 pages) : illustrations.

10.1007/978-1-4842-5373-1 doi
10.1007/978-1-4842-5

QA76.73.P98

005.133

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.

Includes index.

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Online resource; title from PDF title page (SpringerLink, viewed November 18, 2019).

Print version: 9781484253724

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