Ensemble Learning Part 10 | Boosting | Stacking | Machine Learning Tutorial

Stacking often considers heterogeneous weak learners, learns them in parallel and combines them by training a meta-model to output a prediction based on the different weak model predictions. On the other hand, boosting often considers homogeneous weak learners, learns them sequentially in a very adaptative way (a base model depends on the previous ones) and combines them following a deterministic strategy. In this video, you will explore Stacking and Boosting in Ensemble Learning Models. It is the tenth part of the Ensemble Learning Playlist. All 14 videos combined teaches Ensemble Learning in an In-Depth Manner. ✅Subscribe to our Channel to learn more about AI, ML and Data Science. InsideAIML’s Artificial Intelligence Masters Program provides training in the skills required for a career in AI. You will master Data Science, Deep Learning, TensorFlow, Machine Learning and other AI concepts. The course is designed by IITians and includes projects on advanced algorithms and artificial neural networks. Learn more at: https://insideaiml.com/courses For more updates on courses and tips follow us on: - Telegram: https://t.me/insideaiml - Instagram: https://www.instagram.com/inside_aiml/ - Twitter: https://twitter.com/insideaiml - LinkedIn: https://www.linkedin.com/company/insideaiml - Facebook: https://www.facebook.com/insideaimledu - Youtube: https://www.youtube.com/channel/UCz5qPOuMdz3oXv-gPO3h9Iw #MachineLearning #DataScience #DeepLearning #Python #AI #ArtificialIntelligence #Ensemble Learning #Stacking #Boosting