This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn. After learning some of the theory (and math) behind linear regression, we'll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes. By the end of the course, you'll apply a classification model to predict the presence/absence of heart disease from a patient's health data.
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课程信息
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第 1 门课程(共 4 门)
初级
None
完成时间大约为14 小时
英语(English)
您将学到的内容有
Employ artificial intelligence techniques to test hypothesis in Python
Apply a machine learning model combining Numpy, Pandas, and Scikit-Learn
您将获得的技能
- Data Science
- Machine Learning
- regression
- Statistical Hypothesis Testing
- medical data
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 1 门课程(共 4 门)
初级
None
完成时间大约为14 小时
英语(English)
提供方
授课大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
Introduction to Python Programming for Hypothesis Testing
完成时间为 3 小时
9 个视频 (总计 30 分钟), 5 个阅读材料, 6 个测验
完成时间为 5 小时
Creating a Hypothesis: Numpy, Pandas, and Scikit-Learn
完成时间为 5 小时
8 个视频 (总计 35 分钟), 5 个阅读材料, 5 个测验
完成时间为 2 小时
Scikit-Learn Revisited: ML for Hypothesis Testing
完成时间为 2 小时
6 个视频 (总计 22 分钟), 3 个阅读材料, 4 个测验
完成时间为 3 小时
Using Classification to Predict the Presence of Heart Disease
完成时间为 3 小时
1 个视频 (总计 4 分钟)
审阅
- 5 stars54.83%
- 4 stars16.12%
- 3 stars9.67%
- 2 stars9.67%
- 1 star9.67%
来自INTRODUCTION TO DATA SCIENCE AND SCIKIT-LEARN IN PYTHON的热门评论
由 AG 提供Nov 27, 2021
Good introduction. A bit too short for a 4-week course. The autograder is not very good, and some solutions are wrong.
由 DH 提供Apr 4, 2022
The topic is great, and the linkage and references provided are valuable.
The hands-on quiz should be supported with better instructions and descriptions regarding what to do.
由 CT 提供Jan 30, 2022
It could be better if we can see where we did wrong after each assignment. Good and well-paced course otherwise
由 RZ 提供Nov 9, 2021
meskipun agak eror dalam lab penugasan tapi alhamdulillah sudah bisa
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