by Arish Ali | Released September 2018 | ISBN: 9781788392716

Combine multiple machine learning algorithms to build models with higher accuracyAbout This VideoYou will get an intuitive understanding of ensemble learning and we supply practical solutions to real-world problemsGet hands-on with various machine learning techniques along with real-world examplesGet hands-on exposure to many different machine learning models and learn to combine them to solve problemsIn DetailEnsemble 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- Show and hide more

1. Chapter 1 : Getting Started with Ensemble Learning
   • The Course Overview 00:01:31
   • Introduction to Ensemble Learning 00:02:26
   • Setting Up Python 00:03:18
   • Setting Up Dependencies 00:02:40
2. Chapter 2 : Implementing Simple Ensemble Techniques in Python
   • Problems that Ensemble Learning Solves 00:02:56
   • Ensemble Learning for Classification 00:01:40
   • Implementing Ensemble Learning for Classification 00:04:31
   • Ensemble Learning for Regression 00:01:31
   • Implementing Ensemble Learning for Regression 00:01:41
3. Chapter 3 : Creating Robust Models with Bagging Technique
   • Basics of Bagging 00:04:42
   • How Bagging Works 00:02:07
   • Making Predictions on Movie Ratings Using SVM 00:07:59
   • Random Forest 00:06:08
   • Using Random Forest to Analyze Sonar Chirp Data 00:04:58
   • Using the Decision Tree to Determine Weight at Birth 00:02:35
4. Chapter 4 : Converting Weak Models to Strong Models Using Boosting
   • Introduction to Boosting 00:05:32
   • AdaBoost Algorithm 00:05:24
   • Other Boosting Algorithms 00:02:29
   • Predicting Churn Using Boosting 00:02:22
5. Chapter 5 : Stacking Models Together
   • Overview of Stacking Technique 00:03:50
   • Implementing Blending in Python 00:02:19
   • How to Use Stacking 00:02:48
6. Chapter 6 : Ensembling to Win Competitions
   • Practical Advice on Using Different Ensemble Learning Techniques 00:02:18
   • Combining Different Ensemble Models Together 00:02:27
   • Practical Example on Kaggle Competition 00:02:54
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