Probabilistic Graphical Models (CourseraArchive) 5 个评论 关注

开始时间: 04/22/2022 持续时间: 11 weeks

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

简介: In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

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。]

Introduction to Big Data with Apache Spark (EdxArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: 5 weeks

主页: https://www.edx.org/archive/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x

简介: Learn how to apply data science techniques using parallel programming in Apache Spark to explore big (and small) data.

Probabilistic Graphical Models Specialization (Coursera专项课程) 0 个评论 关注

开始时间: 12/21/2023 持续时间: Approximately 4 months to complete Suggested pace of 11 hours/week

主页: https://www.coursera.org/specializations/probabilistic-graphical-models

简介: 斯坦福大学 Daphne Koller 教授的概率图模型专项课程系列（Probabilistic Graphical Models Specialization），作为Coursera上最早的几门课程之一，这门概率图模型课程的相当有难度，完成作业拿到证书还是很有挑战的，感兴趣的同学可以关注：Probabilistic Graphical Models-Master a new way of reasoning and learning in complex domains

Machine Learning and Reinforcement Learning in Finance Specialization (Coursera专项课程) 0 个评论 关注

开始时间: 12/21/2023 持续时间: Approximately 4 months to complete Suggested pace of 5 hours/week

主页: https://www.coursera.org/specializations/machine-learning-reinforcement-finance

简介: 金融中的机器学习和强化学习（Machine Learning and Reinforcement Learning in Finance Specialization）, 这个系列包含4门子课程，主要目标是为金融相关的机器学习核心范式和算法奠定坚实的基础而提供必要的知识和实战技能，特别关注机器学习在金融投资中不同的实际问题中的应用。 感兴趣的同学可以关注：Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets.