| 书目名称 | Data Science Solutions with Python | | 副标题 | Fast and Scalable Mo | | 编辑 | Tshepo Chris Nokeri | | 视频video | | | 概述 | Explains techniques for integrating frameworks for high model performance.Presents a hybrid approach for rapid prototyping models, deploying and scaling them.Bridges the gap between machine and deep l | | 图书封面 |  | | 描述 | Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. .The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras.. .The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Pr | | 出版日期 | Book 2022 | | 关键词 | Big Data Analytics; Machine Learning; Deep Learning; Python; Python Frameworks; Keras; Scikit-learn; PySpar | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-7762-1 | | isbn_softcover | 978-1-4842-7761-4 | | isbn_ebook | 978-1-4842-7762-1 | | copyright | Tshepo Chris Nokeri 2022 |
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发表于 2025-3-21 17:52:03
