May 2025
Intermediate to advanced
328 pages
4h 32m
Chinese
Overview
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采用低代码人工智能（Low-Code AI）的数据优先、用例驱动的方法来理解机器学习和深度学习的概念。本实践指南介绍了三种以问题为中心的学习方法：使用 AutoML 学习无代码机器学习、使用 BigQuery ML 学习低代码机器学习，以及使用 scikit-learn 和 Keras 学习自定义代码。在每种情况下，您都将通过使用具有现实问题的真实数据集来学习关键的机器学习概念。
业务分析师和数据分析师将通过一个详细的数据驱动方法，以项目为基础了解机器学习/人工智能：加载和分析数据；将数据输入机器学习模型；构建、训练和测试模型；以及将模型部署到生产环境。作者迈克尔·阿贝尔（Michael Abel）和格温多林·斯特里普林（Gwendolyn Stripling）将向您展示如何为零售、医疗保健、金融服务、能源和电信行业构建机器学习模型。
您将学会：
- 区分结构化数据与非结构化数据及其带来的挑战
- 可视化并分析数据
- 对数据进行预处理以供机器学习模型使用
- 区分回归和分类监督学习模型
- 比较不同类型的机器学习模型和架构，从无代码到低代码再到定制训练
- 设计、实现并调优机器学习模型
- 将数据导出到 GitHub 仓库以进行数据管理和治理
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