AI Publishing LLC, 2021. — 339 p. — ISBN 978-1-7347901-5-3. Python for Data Scientists — Scikit-Learn Specialization Scikit-Learn, also known as Sklearn, is a free, open-source machine learning (ML) library used for the Python language. In February 2010, this library was first made public. And in less than three years, it became one of the most popular machine learning...
Packt Publishing, 2020. — 399 p. — ISBN: 978-1838826048. Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The...
New York: Packt Publishing, 2017. — 368 p. — ISBN13: 978-1787286382. Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications. About This Book • Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn • Perform supervised and unsupervised learning with ease, and evaluate the performance of...
Amazon Digital Services LLC, 2018. This book is a guide for you on how to use Scikit-Learn, a machine learning library for Python programming language. The author first helps you know what Scikit-Learn are and how to set it up on your system. You are also guided on how to load datasets into Scikit-Learn. The author has then guided you on how to use the various machine learning...
Packt Publishing, 2017. — 530 p. — ISBN: 978-1788833479. About This Book Use Python and scikit-learn to create intelligent applications Discover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domain A practical, example-based guide to help you gain expertise in implementing and evaluating machine learning...
2nd ed. — Packt Publishing, 2017. — 254 p. — ISBN: 978-1788299879. Key Features Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real...
Apress, 2019. — 208 p. — ISBN13: (electronic): 978-1-4842-5373-1. 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....
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