Detecting Heart Disease with Ensemble Machine Learning Methods

This course is focusing on explaining ensemble methods. It will start with an introduction to machine learning and some important concepts. Next, the Decision tree model will be explained. Then, the students will know how an ensemble works, why it is important, and how to use it. Finally, some famous ensemble models will be introduced such as random-forest,  Xgboost, Adaboost.

Objective

• Know the idea behind ensemble learning
• The advantages and the disadvantages of ensemble learning
• Understand how Random forest, Xgboost, Adaboost works
• Know how to train, test, and evaluate them
• Application of ensemble methods to solve real-world problems

• The participants will learn Machine learning models with a real-world case study
• The participants will get hands-on coding experience on Decision trees, Ensemble learning, and Random forest, xgboost and adaboost, etc.
• They will be able to apply them with a real life case study
• An end to end solution of  the heart disease classification Heart Disease UCI | Kaggle

 

• Basic programming skills in python
• The basics of machine learning such as classification, regression.

• Introduction about machine learning
• Introduction and application of Decision Tree Classification
• Introduction and application of Ensemble learning methods
• Introduction and application of Random forest
• Introduction and application of Xgboost and Adaboost
• Application to solve real-life classification problems in healthcare