Supervised Machine Learning in Python

A practical course about supervised machine learning using Python programming language

What you'll learn:
Regression and classification models
Linear models
Decision trees
Naive Bayes
k-nearest neighbors
Support Vector Machines
Neural networks
Random Forest
Gradient Boosting
XGBoost
Voting
Stacking
Performance metrics (RMSE, MAPE, Accuracy, Precision, ROC Curve…)
Feature importance
SHAP
Recursive Feature Elimination
Hyperparameter tuning
Cross-validation

Requirements
Python porgramming language
Data pre-processing techniques

Description
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

Support Vector Machines

Linear SVM

Non-linear SVM

Feedforward neural networks

Ensemble models

Bias-variance tradeoff

Bagging and Random Forest

Boosting and Gradient Boosting

Voting

Stacking

Performance metrics

Regression

Root Mean Squared Error

Mean Absolute Error

Mean Absolute Percentage Error

Classification

Confusion matrix

Accuracy and balanced accuracy

Precision

Recall

ROC Curve and the area under it

Multi-class metrics

Feature importance

How to calculate feature importance according to a model

SHAP technique for calculating feature importance according to every model

Recursive Feature Elimination for dimensionality reduction

Hyperparameter tuning

k-fold cross-validation

Grid search

Random search

All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.

Who this course is for
Python developers
Data Scientists
Computer engineers
Researchers
Students