## CatBoost !

Discover in this article how to use the latest open-source gradient boosting algorithms: the CatBoost!

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# Category: Modeling

## CatBoost !

## XGBoost: The super star of algorithms in ML competition

## Feature Scaling

## Machine Learning Hyper-Parameters Tuning

## Kaggle: Let’s start with the Titanic! (Part 1)

## Evaluate your binary classification model

## Linear Regression

## Logistic regression

All to understand and practice A.I. in a simple way

Discover in this article how to use the latest open-source gradient boosting algorithms: the CatBoost!

Find out in this article why XGBoost is the star of Machine Learning competitions … and especially how to use it!

This article explains in practice why and how to scale (Feature Scaling) the characteristics of a Machine Learning model implemented with Scikit-Learn.

After doing feature engineering, it’s time to fine-tune the hyperparameter so as to get the best predictions!

Engage in your first Kaggle competition and get 75% with the Titanic!

Find out in this article how to evaluate and therefore be able to optimize your ML classification model.

Discover in a simple and fun way how a Linear Regression algorithm works.

This article shows a simple example of logistic regression application