Scikit-Learn

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...

Packt Publishing Ltd., 2020. — 368 p. — ISBN: 978-1-83882-604-8. Code files only! Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular...

New York: Packt Publishing, 2017. — 368 p. About This Book Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works Use Scikit-Learn to simplify the programming side data so you can focus on thinking Discover how to apply algorithms in a variety of situations Who This Book Is For If you're a data scientist...

Packt Publishing, 2017. — 374 p. Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs...

Packt Publishing, 2017. — 530 p. — ISBN: 978-1788833479. 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 systems using...

Packt Publishing, 2014. — 238 p. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and...

Packt Publishing, 2014. — 221 p. — ISBN: 978-1-78398-836-5. 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 life...

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...

2nd ed. — Packt Publishing, 2017. — 368 p. — ISBN: 178728638X. True PDF Data Analysts already familiar with Python but not so much with scikit-learn, who want quick solutions to the common machine learning problems will find this book to be very useful. If you are a Python programmer who wants to take a dive into the world of machine learning in a practical manner, this book...

Packt Publishing, 2014. — 214 p. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across. The book starts by walking...

Packt Publishing, 2020. — 164 p. — ISBN: 978-1-78934-370-0. Code files only! Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book...

Apress, 2019. — 246 p. — ISBN: 1484253728. 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...

Методические рекомендации. — Воронеж : Воронежский институт МВД России, 2021. — 51 с. В методических рекомендациях приводится методика обработки данных экспертных оценок с использованием оригинальных алгоритмов на основе slice-матриц. Предназначены для слушателей факультета переподготовки и повышения квалификации, обучающихся по дополнительным профессиональным программам...