Employing Ensemble Methods with scikit-learn

This course covers the theoretical and practical aspects of building ensemble learning solutions in scikit-learn; from random forests built using bagging and pasting to adaptive and gradient boosting and model stacking and hyperparameter tuning.
Course info
Level
Advanced
Updated
Aug 12, 2019
Duration
2h 15m
Table of contents
Course Overview
Understanding Ensemble Learning Techniques
Implementing Ensemble Learning Using Averaging Methods
Implementing Ensemble Learning Using Boosting Methods
Implementing Ensemble Learning Using Model Stacking
Description
Course info
Level
Advanced
Updated
Aug 12, 2019
Duration
2h 15m
Description

Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. In particular, scikit-learn features extremely comprehensive support for ensemble learning, an important technique to mitigate overfitting. In this course, Employing Ensemble Methods with scikit-learn, you will gain the ability to construct several important types of ensemble learning models. First, you will learn decision trees and random forests are ideal building blocks for ensemble learning, and how hard voting and soft voting can be used in an ensemble model. Next, you will discover how bagging and pasting can be used to control the manner in which individual learners in the ensemble are trained. Finally, you will round out your knowledge by utilizing model stacking to combine the output of individual learners. When you’re finished with this course, you will have the skills and knowledge to design and implement sophisticated ensemble learning techniques using the support provided by the scikit-learn framework.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi, and welcome to this course on Employing Ensemble Methods with scikit-learn. A little about myself, I have a masters degree in electrical engineering from Stanford, and I've worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on realtime collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. Even as the number of machine-learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. In particular, scikit-learn features extremely comprehensive support for ensemble learning, an important technique to mitigate overfitting. In this course, you will gain the ability to construct several important types of ensemble learning models. First, you will learn decision trees and random forests of ideal building blocks for ensemble learning, and how hard voting and soft voting can be used in an ensemble model. You will understand the differences between averaging techniques and boosting techniques to build an ensemble. Next, you will discover how bagging and pasting can be used to control the manner in which individual learners in the ensemble are trained. You will also construct boosting models using both adaptive boosting, as well as gradient boosting techniques. Finally, you will round out your knowledge by utilizing model stacking to combine the output of individual learners. When you're finished with this course, you will have the skills and knowledge to design and implement sophisticated ensemble learning techniques using the support provided by the scikit-learn framework.