|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-imbalanced-data/
课程评论：没有评论
**Coursera 课程总结：不平衡数据集的机器学习** 本课程专为希望提高机器学习模型在不平衡数据集上表现的学习者设计。课程将深入讲解多种常用技术，帮助您有效应对数据不平衡问题，无论是您目前正面临此挑战，还是希望深入了解相关方法。 课程内容全面，步骤清晰，通过生动有趣的视频教程，您将系统学习以下关键知识点： * **欠采样（Under-sampling）技术：** 包括随机欠采样以及针对特定样本人群的聚焦欠采样方法。 * **过采样（Over-sampling）技术：** 涵盖随机过采样以及通过生成新样本来增加少数类的数据量。 * **集成学习（Ensemble Methods）：** 学习如何结合多个弱学习器与采样技术，以提升模型整体性能。 * **代价敏感学习（Cost-Sensitive Methods）：** 掌握如何对少数类误分类给予更严厉的惩罚，从而优化模型决策。 * **评估指标：** 了解在不平衡数据集上评估模型性能的恰当指标。 通过本课程，您将能够根据具体数据集选择合适的技术，并能够应用和比较不同方法带来的性能提升。课程包含 50 多个讲座，总时长超过 10 小时，所有主题都配有实际的 Python 代码示例，方便您参考、练习和应用于自己的项目中。代码将定期更新，以保持与最新趋势和 Python 库的同步。 立即加入，学习如何处理不平衡数据集，构建更出色的机器学习模型！
Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models.If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how.We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique. Specifically, you will learn:Under-sampling methods at random or focused on highlighting certain sample populationsOver-sampling methods at random and those which create new examples based of existing observationsEnsemble methods that leverage the power of multiple weak learners in conjunction with sampling techniques to boost model performanceCost sensitive methods which penalize wrong decisions more severely for minority classesThe appropriate metrics to evaluate model performance on imbalanced datasetsBy the end of the course, you will be able to decide which technique is suitable for your dataset, and / or apply and compare the improvement in performance returned by the different methods on multiple datasets.This comprehensive machine learning course includes over 50 lectures spanning more than 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.In addition, the code is updated regularly to keep up with new trends and new Python library releases.So what are you waiting for? Enroll today, learn how to work with imbalanced datasets and build better machine learning models.
