|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/time-series-classification-in-python/
课程评论：没有评论
课程名称：Python中的时间序列分类 概述：掌握Python中的时间序列分类！本课程涵盖了机器学习和深度学习技术在时间序列分类中的应用，通过100% Python的实践项目进行指导。课程结束时，您将能够：掌握时间序列分类；进行特征工程和模型优化；学习和实现最先进的机器学习和深度学习模型；在医疗保健、物联网、传感器数据、光谱学等领域获得真实数据集的实践经验。 这是时间序列分类中最全面的课程！我们涵盖了各种模型，包括：距离基础模型、字典基础模型、集成模型、特征基础模型、区间基础模型、核基础模型、形状基模型、元分类器。我们先探讨每个模型的理论及其内部工作原理，然后在Python的实践项目中应用它们。此外，还有一个额外模块涵盖深度学习模型，为您提供时间序列分类的深度学习架构应用蓝图。所有函数都是灵活的，能够处理任何数量的特征、样本和时间步的系列数据。 详细大纲： 1. 时间序列分类介绍 2. 时间序列分类应用 3. 基准分类器 4. 距离基础方法 - 欧几里得距离 - K最近邻分类器 - 动态时间规整（DTW）基础 - 形状DTW 5. 字典基础模型 - BOSS - WEASEL - TDEM - UCR 6. 课题项目：日语元音发音人分类 7. 集成方法 - 装袋 - 加权分类器 - 时间序列森林 8. 特征基础方法 - 概述分类器 - 矩阵剖面 - Catch22 - TSFresh 9. 课题项目：加工厂设备故障分类 10. 区间基础方法 - RISE - CIF - DrCIF 11. 核基础方法 - 支持向量机 - Rocket - Arsenal 12. 课题项目：根据电力使用情况分类家电 13. 形状基模型 - 形状变换分类器 14. 混合模型 - HIVE-COTE 15. 课题项目：通过光谱分类饮料 16. 附加模块：时间序列分类的深度学习 - 使用Keras的深度学习蓝图 - 使用PyTorch的深度学习蓝图 本课程非常适合希望深入理解时间序列分类及相关技术的学习者。
Master time series classification in Python! This course covers machine learning and deep learning techniques for classifying time series, all applied in guided hands-on projects in 100% Python.By the end of this course, you will:master time series classificationperform feature engineering and model optimization for classificationlearn and implement state-of-the-art machine learning and deep learning modelsget hands-on experience with real-life datasets in the fields of healthcare, IoT, sensor data, spectroscopy and moreThis is the most complete course on time series classification! We cover all types of models like:Distance-basedDictionary-basedEnsemble modelsFeature-basedInterval-basedKernel-basedShapelet modelsMeta classifiersWe first explore the theory and inner workings of each model before applying them in a hands-on project using Python.Plus, get an additional section covering deep learning models, giving you a blueprint to apply any deep learning architecture for time series classification. All functions are flexible such that you can handle series with any number of features, samples and time steps.Detailed outline:Introduction to time series classificationApplication of time series classificationBaseline classifiersDistance-based methodEuclidean distanceK-Nearest Neighbors classifierDynamic Time Warping (DTW) from scratchShapeDTWDictionary-based modelsBOSSWEASELTDEMUSECapstone project: Japanese vowels' speakers classificationEnsemble methodsBaggingWeighted classifierTime series forestFeature-based methodsSummary classifierMatrix profileCatch22TSFreshCapstone project: Classify equipment failure in a processing plantInterval-based methodRISECIFDrCIFKernel-based methodsSupport vector machineRocketArsenalCapstone project: Classify appliances by their electricity usageShapelet-based methodsShapelet transform classifierHybrid modelsHIVE-COTECapstone project: Beverage classification through spectroscopyEXTRA: Deep learning for time series classificationIn this module, we develop a blueprint such that you can apply any deep learning architectures for time series classification. By the end, you will have built flexible functions that can adapt to series with any number of samples, features and time steps.Deep learning blueprint with KerasDeep learning blueprint with PyTorch
