Machine Learning

In this folder, I present some Machine Learning models that are part of my training. To build a Machine Learning model some steps are important, such as the problem’s analysis, feature engineering and model selection. Each of these steps are presented in this folder.

In this field, we have different applicability that require different algorithm models. Some of these algorithms are presented here according with type of problem. The Machine Learning models covered in this folder are: Linear Regression, Classification, Clustering, Association Rules, Reinforcement Learn and Neutral Language processing.

This repository is organized in the follow order

1 - Data preprocessing

2 - Regression Models

3 - Classification Models

4 - Clustering Models

5 - Association Rules Models

6 - Reiforcement Learn Models

7 - Neutral Language Processing

Note: All folders above contain commented codes inside the folder called Models Explanation. I don’t present a mathematical explanation of each algorithm, it is not the objective here, but I write briefly how I understand the algorithm and how I apply it in Python.