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Machine Learning

This series of tutorials shows you how to calculate and evaluate the different Mathematical Equations (algorithms) you compute to predict the future based on a Data Table.

Articles in this series

#05 | The k-Means & Unsupervised Clustering Models

Sep 6, 20229 min read 482 views

Challenge Importance The time has come to add another layer to the hierarchy of Machine Learning models. Do we have the variable we want to predict in...

#05 | The k-Means & Unsupervised Clustering Models

#04 | Overfitting & Hyperparameter Tuning with Cross Validation

Aug 26, 20229 min read 727 views

Learn how to compute the best configuration of hyperparameters for the same Machine Learning model/algorithm step-by-step. · Chapter Importance We have...

#04 | Overfitting & Hyperparameter Tuning with Cross Validation

#03 | Train Test Split for Model Selection

Aug 24, 20229 min read 362 views

Why do you need to separate the data into train and test? Follow this tutorial to understand the necessity to validate the model on test data. · © Jesús...

#03 | Train Test Split for Model Selection

#01 | The Linear Regression & Supervised Regression Models

Jul 27, 20229 min read 2.0K views

Dive into the essence of Machine Learning by developing several Regression models with a practical use case in Python to predict accidents in the USA. ·...

#01 | The Linear Regression & Supervised Regression Models

#02 | The Decision Tree Classifier & Supervised Classification Models

May 6, 202211 min read 1.3K views

This tutorial shows you the step-by-step resolution of possible errors you may get as you develop a Decision Tree Classifier. · © Jesús López 2022 Don't...

#02 | The Decision Tree Classifier & Supervised Classification Models

#06 | The Principal Component Analysis (PCA) & Dimensionality Reduction Techniques

Nov 8, 20217 min read 1.1K views

Learn how to group variables to plot seven variables in a 2-dimensional plot and why PCA is an essential technique to visualize clustering models. · ©...

#06 | The Principal Component Analysis (PCA) & Dimensionality Reduction Techniques