July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Overview
本书提供了约200个独立的解决方案，来帮助你解决在日常开发中可能遇到的机器学习方面的问题。如果你熟悉Python和它的库（包括Pandas和scikit-learn），你就能应对加载数据、处理文本或数值型数据、模型选择、降维等特定问题。
书中的每个解决方案都给出了相应的代码，你可以直接把它们复制并粘贴到测试数据集中，看看它们是否能正常运行。接下来，你还可以对这些代码进行插入、组合和修改操作，以构建自己的应用。除了代码，书中还对每个解决方案进行了讨论，解释其原理并介绍相关的背景知识。本书的重点不在于讲解机器学习的理论和概念，而在于帮助你掌握构建实用的机器学习应用的要点。
本书包含如下方面的解决方案：
- 向量、矩阵和数组
- 处理数值型数据和分类数据，处理文本、图像，处理时间和日期
- 用特征提取或特征选择方法进行降维
- 模型的评估及选择
- 线性回归和逻辑回归、树和森林、K近邻
- 支持向量机（SVM）、朴素贝叶斯、聚类和神经网络
- 保存和加载训练后的模型
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