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所在平台: Udemy |
课程主页: https://www.udemy.com/course/binary-classification-explainability-for-data-science/
课程评论：没有评论
《机器学习与可解释性在数据科学中的应用》课程总结 本课程将带领您从头到尾构建一个二元分类机器学习模型，用于预测一个人是否正在寻找新工作。您将经历一个完整的机器学习项目流程，包括： * **数据收集与探索：** 学习如何获取和理解数据集。 * **特征工程：** 创造和选择最能代表问题的特征。 * **模型选择与数据转换：** 根据项目需求选择合适的模型并对数据进行预处理。 * **模型训练与评估：** 训练模型并评估其性能。 * **模型可解释性：** 理解模型是如何做出预测的，并解释哪些特征对预测结果至关重要。 通过这个项目，您将能够理解并解释哪些因素决定了一个人是否在寻找新工作。本课程提供的 Jupyter Notebook 模板可以应用于许多其他的二元分类场景，例如预测用户行为、产品选择、用户注册意愿等，使您能够将所学知识应用到各种实际项目中。 **目标受众：** 具备初级到高级 Python 和数据科学知识的学习者。对于初学者，可以积极提问；对于有经验的学习者，本课程是模型可解释性方面一个极佳的复习和实践机会。 课程旨在让学习者在享受项目实践的同时，掌握机器学习的核心流程和可解释性技术。
You will build a binary classification machine learning model to predict if a person is looking for a new job or not. You'll go through the end to end machine learning project- data collection, exploration, feature engineering, model selection, data transformation, model training, model evaluation and model explainability. We will brainstorm ideas throughout each step and by the end of the project you'll be able to explain which features determine if someone is looking for a new job or not.The template of this Jupyter Notebook can be applied to many other binary classification use cases. Questions like - will X or Y happen, will a user choose A or B, will a person sign up for my product (yes or no), etc. You will be able to apply the concepts learned here to many useful projects throughout your organization!This course is best for those with beginner to senior level Python and Data Science understanding. For more beginner levels, feel free to dive in and ask questions along the way. For more advanced levels, this can be a good refresher on model explainability, especially if you have limited experience with this. Hopefully you all enjoy this course and have fun with this project!
