Pattern Discovery in Data Mining (CourseraArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: 4 weeks

主页: https://www.coursera.org/course/patterndiscovery

简介: Learn the basic concepts of data mining and dive deep into pattern discovery methods and their applications.

機器學習技法 (Machine Learning Techniques) (CourseraArchive) 2 个评论 关注

开始时间: 04/22/2022 持续时间: 8 weeks

主页: https://www.coursera.org/course/ntumltwo

简介: The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

Data Analysis and Statistical Inference (CourseraArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

主页: https://www.coursera.org/course/statistics

简介: This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

Introduction to Programming with MATLAB (CourseraArchive) 0 个评论 关注

开始时间: 04/22/2022 持续时间: 9 weeks

主页: https://www.coursera.org/course/matlab

简介: This course teaches computer programming to those with little to no previous experience. It uses the programming system and language called MATLAB to do so because it is easy to learn, versatile and very useful for engineers and other professionals.

機器學習基石 (Machine Learning Foundations) (CourseraArchive) 10 个评论 关注

开始时间: 04/22/2022 持续时间: 8 weeks

主页: https://www.coursera.org/course/ntumlone

简介: Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]