CourseClub NET Coursera How to Win Data Science Competition Learn from Top Kagglers 005 Feature preprocessing and generation with respect to models 011 Categorical and ordinal features mp4

CourseClub NET Coursera How to Win Data Science Competition Learn from Top Kagglers
005 Feature preprocessing and generation with respect to models 011 Categorical and ordinal features mp4

Name	[CourseClub.NET] Coursera - How to Win a Data Science Competition Learn from Top Kagglers	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	2.2 GB
Total Files	131
Hash	C34A26F7A024C4767216AF3E9C47F26540D7D1D0

/005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.mp4	42.5 MB
/005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.srt	13.5 KB
/005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.mp4	34.0 MB
/005.Feature preprocessing and generation with respect to models/010. Numeric features.mp4	50.7 MB
/005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.srt	10.5 KB
/005.Feature preprocessing and generation with respect to models/010. Numeric features.srt	19.0 KB
/005.Feature preprocessing and generation with respect to models/013. Handling missing values.mp4	39.7 MB
/005.Feature preprocessing and generation with respect to models/013. Handling missing values.srt	13.1 KB
/005.Feature preprocessing and generation with respect to models/009. Overview.mp4	26.9 MB
/005.Feature preprocessing and generation with respect to models/009. Overview.srt	9.2 KB
/006.Feature extraction from text and images/014. Bag of words.mp4	39.9 MB
/006.Feature extraction from text and images/014. Bag of words.srt	14.0 KB
/006.Feature extraction from text and images/015. Word2vec, CNN.mp4	48.2 MB
/001.Welcome to How to win a data science competition/002. Meet your lecturers.mp4	14.5 MB
/006.Feature extraction from text and images/015. Word2vec, CNN.srt	17.2 KB
Showing first 15 files of 131 total files