Merge branch 'main' of https://github.com/SalatielBairros/CaosML

README.md

@@ -2,9 +2,24 @@
 
 ## Introduction
 
-This repository implements some machine learning algorithms from scratch using Python. The main purpose of this repository is to serve as a reference for studying purposes. The algorithms implemented here are:
+This repository implements some machine learning algorithms from scratch using Python. The main purpose of this repository is to serve as a reference for studying purposes.
 
-- Linear Regression
+### Why Caos?
+
+The name "caos" is because all learning is caotic. You'll have joy and frustration at the same time. Also the development of this project itself is surrounded by caos: I have a toddler at home always asking what I am doing.
+
+### Implemented algorithms
 
 Each algorithm has its own folder with a README.md file explaining the algorithm and how to run it. All the explanations are written in Portuguese, but the code is written in English to facilitate the comparison with other implementations.
-More information will be added here in the future as more algorithms are implemented.
+More information will be added here in the future as more algorithms are implemented.
+
+- [Linear Regression](https://github.com/SalatielBairros/CaosML/tree/main/src/regression/linear)
+- [Logistic Regression](https://github.com/SalatielBairros/CaosML/tree/main/src/classification/logistic)
+
+### Current implementing:
+
+- KNN
+
+### Next
+
+- Decision Tree

src/classification/logistic/README.md

@@ -86,7 +86,7 @@ def __fit_loss_minimization__(self,
 
 > Para ver com detalhes as implementações das funções chamadas, veja o código fonte do projeto.
 
-### Minimizando _likehood_
+### Maximizando _likehood_
 
 A Máxima Verossimilhança (Maximum Likelihood Estimation - MLE) para a Regressão Logística é um método utilizado para encontrar os valores dos parâmetros do modelo que maximizam a função de verossimilhança. A ideia fundamental por trás da MLE é escolher os parâmetros que tornam mais provável observar os dados que temos.
 
@@ -189,4 +189,4 @@ Espero que este projeto tenha sido alguma ajuda no entendimento da Regressão Lo
 
 ----
 
-<sup><sup>1. Conclusão (e apenas ela) escrita com o auxílio do Chat GPT 3</sup></sup>
+<sup><sup>1. Conclusão (e apenas ela) escrita com o auxílio do Chat GPT 3</sup></sup>