# Learn when to apply any Machine Learning algorithm

Don't struggle to choose which model is the best for your dataset.

We have strategically designed the exercises to answer the most asked questions to choose the best Machine Learning solution for your problem using Scikit-Learn library.

## This course is for you

You want to learn how to choose best Machine Learning models in Python for any proyect

## Requirements

You only need a computer with Internet conection.

We'll take care of the rest by explaining all concepts through hands-on tutorials step by step.

Our courses have a life of its own. In other words, we add new content that solves the students' most common doubts as if a private lesson was given.

## Key outcomes

###### Learn by solving errors of code

Forget about nice courses where any single line you execute won't have any error. You should learn through trial and error to understand the dos and don'ts, read tutorial.

###### Supervised vs Unsupervised Learning

Why some Algorithms don't ask for the target variable to compute the model's mathematical equation?

###### The only maths algebra you'll need

Don't think you need to go through a precourse of complicated maths equations; the only maths you'll compute are simple vector multiplications.

###### Differentiate Classifiers from Regressors

Most Machine Learning Algorithms have both classifiers and regressors. You'll learn when to use them accordingly to your data.

## Get the free

## sample tutorials

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## Pricing

Flexible options adapted to your needs.

**Digital Edition**