piactical__algorithm

利用python进行数据分析，英文书籍，从pandas库的数据分析工具开始利用高性能工具对数据进行加载、清理、转换、合并以及重塑；利用matpIotlib创建散点图以及静态或交互式的可视化结果；利用pandas的groupby功能对数据集进行切片、切块和汇总操作；处理各种各样的时间序列数据。(Starting from the data analysis tools of pandas database, high performance tools are used to load, clean, transform, merge and remodel data; scatter plots and static or interactive visualization results are created by matpIotlib; data sets are sliced, sliced and aggregated by pandas group by function; and various operations are processed. Time series data.)

说明：  采用机器学习预测房价.使用波士顿房屋信息数据来训练和测试一个模型，并对模型的性能和预测能力进行评估。(Using Machine Learning to Predict House Prices)

说明：  通过时事数据可视化系统，可以清楚地了解全球疫情分布的状况以及密度，以便做出相应的对策(Through the current affairs data visualization system, it is possible to clearly understand the distribution and density of the global epidemic in order to make corresponding countermeasures)

google搜索的核心技术,VC++源码实现,包含测试数据库()

说明：  用python 做的网络数据爬虫，爬取淘宝数据，并分析。(Use Python to do the network data crawler, crawl the Taobao data, and analyze.)

FIR Filter Design This chapter treats the design of linear-pha()

Python数据预处理示例，包括数据清洗、数据整合、数据变换等操作。(Python data preprocessing examples, including data cleaning, data integration, data transformation and other operations.)

说明：  偏最小二乘回归！PLSR，可用于解决一些回归问题！(Partial least squares regression! PLSR can be used to solve some regression problems!)

分类器的性能比较与调优: 使用scikit-learn 包中的tree，贝叶斯，knn，对数据进行模型训练，尽量了解其原理及运用。 使用不同分析三种分类器在实验中的性能比较，分析它们的特点。 本实验采用的数据集为house与segment。(Performance comparison and optimization of classifiers: We use tree, Bayesian and KNN in scikit-learnpackage to train the data model and try to understand its principle and application. The performances of three classifiers are compared and their characteristics are analyzed. The data set used in this experiment is house and segment.)

《从零开始学爬虫》的配套资料（PPT和源码）("Learning Reptiles from Zero" (PPT and Source))