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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- 5 stars58.62%
- 4 stars13.79%
- 3 stars10.34%
- 2 stars6.89%
- 1 star10.34%
INTRODUCTION TO DATA SCIENCE AND SCIKIT-LEARN IN PYTHON의 최상위 리뷰
Good introduction. A bit too short for a 4-week course. The autograder is not very good, and some solutions are wrong.
meskipun agak eror dalam lab penugasan tapi alhamdulillah sudah bisa
It could be better if we can see where we did wrong after each assignment. Good and well-paced course otherwise
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.
AI for Scientific Research 특화 과정 정보
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