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Supervised Machine Learning: Regression

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    Guía de Registro en Coursera

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  • Introduction to Supervised Machine Learning and Linear Regression
    • This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
  • Data Splits and Polynomial Regression
    • There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
  • Cross Validation
    • There is a trade-off between the size of your training set and your testing set. If you use most of your data for training, you will have fewer samples to validate your model. Conversely, if you use more samples for testing, you will have fewer samples to train your model. Cross Validation will allow you to reuse your data to use more samples for training and testing.
  • Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net
    • This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
  • Regularization Details
    • In this section, you will understand the relationship between the loss function and the different regularization types.
  • Final Project
    • In this section you will test everything you learned