Machine learning 101 with Scikit-Learn and StatsModels /

Saved in:
Bibliographic Details
Imprint:[Place of publication not identified] : Packt Publishing, 2019.
Description:1 online resource (1 streaming video file (5 hr., 13 min., 25 sec.))
Language:English
Subject:
Format: E-Resource Video
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/13679121
Hidden Bibliographic Details
Varying Form of Title:Machine learning one hundred and one with Scikit-Learn and StatsModels
Other authors / contributors:365 Careers, issuing body.
Notes:Title from resource description page (Safari, viewed February 18, 2020).
Summary:"Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist!"--Resource description page