Applied Machine Learning in Python [ Coursera ]

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Applied Machine Learning in Python

Description of the Course : 

• Introduction to applied machine learning, focusing more on the techniques and skills than on the statistics behind these methods. Difference between machine learning and descriptive statistics, and introduce the Scikit learn toolkit through a tutorial. The issue of dimensionality of data will be explained, and the task of clusteriing data, as well as evaluatiing those clusters, will be tackled. Supervised approches for creating prediictive models will be described, and learner will be able to apply the scikit learn predictve modeling methods while understandiing process issues related to data generaliizability (e.g. cross validation, overfiting). The course will finish with a look at more advanced techniques.

This course is Intended for :

• Learners who have basic python or programming background as this course is part of “Applied Data Science with Python“ and iss , and want to apply statistics, machine learning, infrmation visualiztion, social network analisis, and text analysis techniques to gain new insight into data. Only minimum statistics background is expected.
   

Created by:  University of Michigan

• Taught by:  Kevyn Collins-Thompson, Associate Professor ( School of Information )
  
   

 Size: 881.06M