[ FreeCourseWeb.com ] Data Cleaning in Python (Updated 5/2020)
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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 39 lectures (3h 13m) | Size: 828.7 MB
Preprocessing, structuring and normalizing data
What you'll learn:
Data cleaning or cleansing as a preprocessing step towards making the data more consistent and high quality before training predictive models.
Requirements
Basics of Python
Description
Data cleaning or Data cleansing is very important from machine learning perspective. The term 'garbage in garbage out' refers to the same fact that without sorting what we have in the data and how to make it more presentable, no matter how good a predictive model is used, the results aren't going to be anything reliable. Beginners with machine learning starts working with the publicly available datasets that are thoroughly analyzed with such issues and are therefore, ready to be used for training models and getting good results. But it is far from how the data is in real world. The datasets that are in raw form and have all such issues cannot be benefited from, without knownig the data cleaning and preprocessing steps.
Such issues may include missing values, noise values or univariate outliers, multivariate outliers, data duplication, improving the quality of data through standardizing and normalizing it, dealing with categorical features. Visualization also happens to be an important tool for manually observing issues in the data.
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