We are working on the course content with rocket speed !! sooner we will be loaded with all the courses

Course 2 : combine models (Ensemble)

About Course

Ensemble models are widely used in data science due to their ability to enhance predictive performance, reduce overfitting, handle complex problems, and provide more robust predictions. They are particularly effective when individual models in the ensemble are diverse and have complementary strengths. However, ensemble models can be computationally expensive, require more training data, and may be more challenging to interpret compared to individual models. Proper selection, tuning, and evaluation of ensemble models are essential for achieving the best results.
Show More

What I will learn?

  • - Idea of Ensemble models in Machine Learning
  • - 3 types of Ensembles

Course Curriculum

BAGGING ensembles


BOOSTING model – Gradient Boosting

Stacking Ensembles

Xtreme Gradient Boosting
Xtreme Gradient Boosting (XGBoost) is a highly optimized implementation of gradient boosting that has gained popularity in machine learning competitions and real-world applications.

Student Ratings & Reviews

No Review Yet
No Review Yet

Material Includes

  • 1. Self learning videos
  • 2. Slides/PDFs
  • 3. Python notebook files
  • 4. Datasets
  • 5. Quizzes
  • 6. Assignments

Target Audience

  • Students
  • Fresh Out of college
  • Working professionals
  • Managers and leads
  • Data Scientists
  • ML practitioners
  • MLOPS engineers