|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/applied-data-science-for-data-analysts
课程评论：没有评论
课程名称：数据分析师的应用数据科学 课程概述：在本课程中，您将提升数据科学技能，解决现实世界中的问题。您将通过数据科学流程，使用无监督学习探索数据，进行特征工程和选择有意义的特征，同时使用基于树的模型解决复杂的监督学习问题。您还将学习应用超参数调整和交叉验证策略，以提高模型性能。 注意：这是Coursera上“面向数据分析师的Databricks数据科学”系列课程的第三门也是最后一门课程。为了在本课程中取得成功，我们强烈建议您在参加此课程之前先修完该系列的前两门课程：Apache Spark for Data Analysts和Data Science Fundamentals for Data Analysts。 课程大纲： - 欢迎来到课程 - 应用无监督学习 - 特征工程与选择 - 应用基于树的模型 - 模型优化
Name:Welcome to the Course
Description:
Name:Applied Unsupervised Learning
Description:
Name:Feature Engineering and Selection
Description:
Name:Applied Tree-based Models
Description:
Name:Model Optimization
Description:
In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance. NOTE: This is the third and final course in the Data Science with Databricks for Data Analysts Coursera specialization. To be successful in this course we highly recommend taking the first two courses in that specialization prior to taking this course. These courses are: Apache Spark for Data Analysts and Data Science Fundamentals for Data Analysts.
