Combine multiple machine learning algorithms to build models with higher accuracy
About This Video
- You will get an intuitive understanding of ensemble learning and we supply practical solutions to real-world problems
- Get hands-on with various machine learning techniques along with real-world examples
- Get hands-on exposure to many different machine learning models and learn to combine them to solve problems
Ensemble is a powerful way to upgrade your model as it combines models and doesn't assume a single model is the most accurate. But what if we combine these models as a way to drop those limitations to produce a much more powerful classifier or regressor?
This course will show you how to combine various models to achieve higher accuracy than base models can. This has been the case in various contests such as Netflix and Kaggle, where the winning solutions used ensemble methods.
If you want more than a superficial look at machine learning models and wish to build reliable models, then this course is for you.
The code bundle is placed at this link https://github.com/PacktPublishing/Ensemble-Machine-Learning-Techniques-
Table of Contents
- Chapter 1 : Getting Started with Ensemble Learning
- Chapter 2 : Implementing Simple Ensemble Techniques in Python
- Chapter 3 : Creating Robust Models with Bagging Technique
- Chapter 4 : Converting Weak Models to Strong Models Using Boosting
- Chapter 5 : Stacking Models Together
- Chapter 6 : Ensembling to Win Competitions
- Title: Ensemble Machine Learning Techniques
- Release date: September 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788392716