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Train a Supervised Machine Learning Model
Building a supervised model is integral to machine learning. In this course, we will learn how to apply classification (decision trees, logistic regression) and regression (k-nearest neighbors, linear regression) algorithms to your data!Moyenne
8 heures
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Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. Supervised machine learning is the underlying method behind a large part of this.
In this course, we will introduce you to the concepts and methods used in supervised learning. You will learn how to build models to make predictions using data.
We will look at two classification algorithms: decision trees and logistic regression. These will allow you to predict categories from your data.
We will also look at two regression algorithms: linear regression and k-nearest neighbors. These will allow you to predict continuous values from your data.
We will take a detailed look at how to apply these techniques in practice using Python code. You will also learn how to clean and prepare your data.
By the end of this course, you will be able to build machine learning models to make predictions using your own data.
Objectifs pédagogiques
- Explain the process of training a model
- Prepare data with feature engineering techniques
- Build a supervised learning model to address a classification task
- Build a supervised learning model to address a regression task
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Table des matières
- Partie 1
Understand the Process of Training a Model
- Partie 2
Prepare Your Data With Feature Engineering
- Partie 3
Build a Classification Model
- Partie 4
Build a Regression Model
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