Ensemble method is a machine learning technique that combines several base models in order to produce one optimal predictive model. Learn the detailed maths and intutuion behind these ensemble methods. Solve data science problems effeciently using multiple ensemble algorthms
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Learning duration ~ 2-3 weeks
Complete access for 1 year
What you will learn
What is ensemble method and why is it so effecient?
Learn various types of ensemble methods
Bias Variance tradeoff using ensemble method
Build your first bagging model - Random Forest
Understand Gradient Boosting, XGBoost etc
Tune the hyperparameters to improve the performance
Interpret the complex ensemble models
Use all cores of laptop/pc to train models fast
Basics of Python
Foundational knowledge of Data Science
High school maths
Who should attend this course?
Data Science Aspirants
Data Science Professionals
Software/Data engineers interested in quantitative analysis
Professionals working with large datasets
Data analysts, economists, researchers
Selva Prabhakaran Principal Data Scientist
My name is Selva, and I am super excited to mentor you on this project!
I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.
I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I’ve put up the series of courses and projects with detailed explanations – just like an on the job experience. Hope you love it!