Mukhopadhyay, Sayan Samanta, Pratip Advanced data analytics using python 9781484280041

Варианты приобретения

Цена: 41920.00T
Кол-во:
Наличие: Поставка под заказ. Есть в наличии на складе поставщика.
Склад Англия: 3 шт. Склад Америка: 243 шт.
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября

Добавить в корзину

в Мои желания

Автор: Mukhopadhyay, Sayan Samanta, Pratip
Название: Advanced data analytics using python
ISBN: 9781484280041
Издательство: Springer
Классификация:

Алгоритмы и структуры данных

Языки программирования и описания сценариев: общие сведения

Сбор и анализ данных

Искусственный интеллект

Машинное обучение

ISBN-10: 1484280040
Обложка/Формат: Paperback
Страницы: 249
Вес: 0.41 кг.
Дата издания: 26.11.2022
Язык: English
Издание: 2nd ed.
Иллюстрации: 32 illustrations, black and white; xvii, 249 p. 32 illus.
Размер: 154 x 234 x 20
Читательская аудитория: Professional & vocational
Подзаголовок: With architectural patterns, text and image classification, and optimization techniques
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. Youll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. Youll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. What Youll Learn * Build intelligent systems for enterprise * Review time series analysis, classifications, regression, and clustering * Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning * Use cloud platforms like GCP and AWS in data analytics * Understand Covers design patterns in Python Who This Book Is For Data scientists and software developers interested in the field of data analytics.
Дополнительное описание: Chapter 1: Overview of Python Language.- Chapter 2: ETL with Python.- Chapter 3: Supervised Learning and Unsupervised Learning with Python.- Chapter 4: Clustering with Python.- Chapter 5: Deep Learning & Neural Networks.- Chapter 6: Time Series Analysis.-

Advanced data analytics using python, Mukhopadhyay, Sayan Samanta, Pratip