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PRIVATE
Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by
Certification

Course Curriculum

    • The roles in a data science project 00:00:00
    • Stages of a data science project 00:00:00
    • Setting expectations 00:00:00
    • Working with data from files 00:00:00
    • Working with relational databases 00:00:00
    • Using summary statistics to spot problems 00:00:00
    • Spotting problems using graphics and visualization 00:00:00
    • Cleaning data 00:00:00
    • Sampling for modeling and validation 00:00:00
    • Mapping problems to machine learning tasks 00:00:00
    • Evaluating models 00:00:00
    • Validating models 00:00:00
    • KDD and KDD Cup 2009 00:00:00
    • Building single-variable models 00:00:00
    • Building models using many variables 00:00:00
    • Using linear regression 00:00:00
    • Using logistic regression 00:00:00
    • Cluster analysis 00:00:00
    • Association rules 00:00:00
    • Using bagging and random forests to reduce training variance 00:00:00
    • Using generalized additive models (GAMs) to learn non-monotone relationships 00:00:00
    • Using kernel methods to increase data separation 00:00:00
    • Using SVMs to model complicated decision boundaries 00:00:00
    • The buzz dataset 00:00:00
    • Using knitr to produce milestone documentation 00:00:00
    • Using comments and version control for running documentation 00:00:00
    • Deploying models 00:00:00
    • Presenting your results to the project sponsor 00:00:00
    • Presenting your model to end users 00:00:00
    • Presenting your work to other data scientists 00:00:00

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