Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine lear 9781838826048

Варианты приобретения

Цена: 53940.00T
Кол-во:
Наличие: Поставка под заказ. Есть в наличии на складе поставщика.
Склад Америка: 281 шт.
При оформлении заказа до: 2025-07-23
Ориентировочная дата поставки: конец Сентября - начало Октября
При условии наличия книги у поставщика.

Добавить в корзину

в Мои желания

Автор: Amr Tarek
Название: Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine lear
ISBN: 9781838826048
Издательство: Packt Publishing
Классификация:

Языки программирования и описания сценариев: общие сведения

Проектирование и теория баз данных

Математическая теория вычислений

Машинное обучение

Информационная архитектура

ISBN-10: 1838826041
Обложка/Формат: Paperback
Страницы: 384
Вес: 0.66 кг.
Дата издания: 24.07.2020
Язык: English
Размер: 23.50 x 19.05 x 2.01 cm
Читательская аудитория: General (us: trade)
Подзаголовок: A practical guide to implementing supervised and unsupervised machine learning algorithms in python
Рейтинг:
Поставляется из: США
Описание:

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

Key Features

Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
Master the art of data-driven problem-solving with hands-on examples
Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms

Book Description

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 book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. Youll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, youll gain a thorough understanding of its theory and learn when to apply it. As you advance, youll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.

By the end of this machine learning book, youll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. Youll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.

What you will learn

Understand when to use supervised, unsupervised, or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
Apply supervised and unsupervised algorithms to overcome various machine learning challenges
Employ best practices for tuning your algorithms hyper parameters
Discover how to use neural networks for classification and regression
Build, evaluate, and deploy your machine learning solutions to production

Who this book is for

This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine lear, Amr Tarek