Ementorhub

What you'll learn

Employ regularization techniques for enhanced model performance and robustness.

Leverage ensemble methods, such as bagging and boosting, to improve predictive accuracy.

Implement hyperparameter tuning and feature engineering to refine models for real-world challenges.

Combine diverse models for superior predictions, expanding your predictive toolkit.

Skills you'll gain

Machine Learning Algorithms

Decision Tree Learning

Artificial Intelligence and Machine Learning (AI/ML)

Supervised Learning

Algorithms

Random Forest Algorithm

Predictive Modeling

Regression Analysis

Machine Learning

Classification And Regression Tree (CART)

Feature Engineering

Performance Tuning

Applied Machine Learning

There are 6 modules in this course

1. Employ regularization techniques for enhanced model performance and robustness. 2. Leverage ensemble methods, such as bagging and boosting, to improve predictive accuracy. 3. Implement hyperparameter tuning and feature engineering to refine models for real-world challenges. 4. Combine diverse models for superior predictions, expanding your predictive toolkit. 5. Strategically select the right machine learning models for different tasks based on factors and parameters.

Ensemble Learning - Bagging Algorithms

Ensemble Learning - Boosting Algorithms

Feature Engineering and Hyperparameter Tuning

Combining Models

Model Selection