Ensemble models in machine learning with Python

Ensemble models in machine learning with Python, available at $39.99, has an average rating of 4.17, with 17 lectures, based on 3 reviews, and has 21 subscribers.

You will learn about Bias variance tradeoff What ensemble models are Bagging and random forest Boosting and XGBoost This course is ideal for individuals who are Python developers or Data scientists or Computer engineers or Researchers or Students It is particularly useful for Python developers or Data scientists or Computer engineers or Researchers or Students.

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Summary

Title: Ensemble models in machine learning with Python

Price: $39.99

Average Rating: 4.17

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Bias variance tradeoff
What ensemble models are
Bagging and random forest
Boosting and XGBoost

Who Should Attend

Python developers
Data scientists
Computer engineers
Researchers
Students

Target Audiences

Python developers
Data scientists
Computer engineers
Researchers
Students

In this practicalcourse, we are going to focus on ensemble models in supervised machine learningusing Python programming language.

Ensemble models are a particular kind of machine learning model that mixes several models together. The general idea is that a team of models is able to increase the performance of a single one, both in terms of stability (i.e. variance) and in terms of accuracy (i.e. bias). The most common ensemble models are Random Forests and Gradient Boosting Decision Trees, which are explained extensively in the lessons of this course. Other types of ensemble models are voting and stacking, which are more complex procedures that are able to increase the performance of a model.

With this course, you are going to learn:

What bias-variance tradeoff is and how to deal with it
Bagging and some bagging models (like Random Forest)
Boosting and some boosting models (Like XGBoostor AdaBoost)
Voting
Stacking

All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.

This course is part of my Supervised Machine Learning in Python online course, so you’ll find some lessons that are already included in the larger course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Ensemble models and bias-variance tradeoff

Chapter 2: Bagging

Lecture 1: Introduction to bagging

Lecture 2: Bagging in Python

Lecture 3: Introduction to Random Forest

Lecture 4: Random Forest in Python

Lecture 5: Introduction to Extremely Randomized Trees

Lecture 6: Extremely Randomized Trees in Python

Chapter 3: Boosting

Lecture 1: Introduction to boosting

Lecture 2: Boosting in Python

Lecture 3: Introduction to Gradient Boosting

Lecture 4: Gradient Boosting in Python

Lecture 5: XGBoost in Python

Chapter 4: Voting

Lecture 1: Introduction to voting

Lecture 2: Voting in Python

Chapter 5: Stacking

Lecture 1: Introduction to stacking

Lecture 2: Stacking in Python

Instructors

Gianluca Malato
Your Data Teacher

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4 stars: 1 votes
5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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