Regression | Supervised Machine Learning with scikit-learn
In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.
Datasets
https://drive.google.com/drive/folder...
Links
Python: https://www.python.org/downloads/
PyCharm: https://www.jetbrains.com/pycharm/dow...
scikit-learn: https://pypi.org/project/scikit-learn/
pandas: https://pypi.org/project/pandas/
numpy: https://pypi.org/project/numpy/
matplotlib: https://pypi.org/project/matplotlib/
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00:00 Course introduction
00:36 Chapter introduction
01:07 Introduction to regression
04:57 The basics of linear regression
08:17 Cross Validation
11:18 Regularized regression
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