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Titlebook: Distributed Machine Learning with PySpark; Migrating Effortless Abdelaziz Testas Book 2023 Abdelaziz Testas 2023 Python.Scalable machine le

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51#
發(fā)表于 2025-3-30 10:23:37 | 只看該作者
52#
發(fā)表于 2025-3-30 15:57:37 | 只看該作者
53#
發(fā)表于 2025-3-30 18:18:44 | 只看該作者
Natural Language Processing with Pandas, Scikit-Learn, and PySpark, learning is known as natural language processing (NLP), which finds uses in many business applications including speech recognition, chatbots, language translation, and email spam detection (ham or spam).
54#
發(fā)表于 2025-3-30 21:15:48 | 只看該作者
k-Means Clustering with Pandas, Scikit-Learn, and PySpark,ering is a commonly used technique in segmentation analysis to group similar observations together based on their characteristics or their proximity in the feature space. The result is a set of clusters, with each observation assigned to a specific cluster. By organizing data into clusters, we can g
55#
發(fā)表于 2025-3-31 01:24:30 | 只看該作者
56#
發(fā)表于 2025-3-31 07:44:12 | 只看該作者
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發(fā)表于 2025-3-31 13:16:58 | 只看該作者
Book 2023o make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools...Distributed Machine Learning with PySpark. offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn)
58#
發(fā)表于 2025-3-31 14:33:41 | 只看該作者
Decision Tree Regression with Pandas, Scikit-Learn, and PySpark, of property and the number of bedrooms, bathrooms, and stories, among others. Additionally, we will compare the performance of Pandas and PySpark in data loading and exploration tasks to better understand their similarities and differences.
59#
發(fā)表于 2025-3-31 20:19:03 | 只看該作者
60#
發(fā)表于 2025-3-31 22:27:11 | 只看該作者
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