Supervised Machine Learning in Python

In this practical course, we are going to focus on supervised machine learning and how to apply it in Python programming language.

Supervised machine learning is a branch of artificial intelligence whose goal is to create predictive models starting from a dataset. With the proper optimization of the models, it is possible to create mathematical representations of our data in order to extract the information that is hidden inside our database and use it for making inferences and predictions.

A very powerful use of supervised machine learning is the calculation of feature importance, which makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common approach for calculating feature importance is the SHAP technique.

Finally, the proper optimization of a model is possible using some hyperparameter tuning techniques that make use of cross–validation.

With this course, you are going to learn:

What supervised machine learning is

What overfitting and underfitting are and how to avoid them

The difference between regression and classification models

Linear models

Linear regression

Lasso regression

Ridge regression

Elastic Net regression

Logistic regression

Decision trees

Naive Bayes

K–nearest neighbors