Ementorhub

What you'll learn

Understand the principles and significance of regression analysis in supervised learning.

Implement cross-validation methods to assess model performance and optimize hyperparameters.

Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.

Skills you'll gain

Data Analysis

Feature Engineering

Statistical Modeling

Machine Learning Methods

Statistical Analysis

Supervised Learning

Predictive Modeling

Regression Analysis

Exploratory Data Analysis

Scikit Learn (Machine Learning Library)

There are 6 modules in this course

By the end of this course, students will be able to: 1. Understand the principles and significance of regression analysis in supervised learning. 2. Grasp the concepts and applications of linear regression and its interpretation in real-world datasets. 3. Explore polynomial regression to capture nonlinear relationships between variables. 4. Apply regularization techniques (Ridge, Lasso, and Elastic Net) to prevent overfitting and improve model generalization. 5. Implement cross-validation methods to assess model performance and optimize hyperparameters. 6. Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy. 7. Evaluate and compare the performance of different regression models using appropriate metrics. 8. Apply regression analysis techniques to real-world case studies, making data-driven decisions. Throughout the course, students will actively engage in tutorials and case studies, strengthening their regression analysis skills and gaining practical experience in applying regression techniques to diverse datasets. By achieving the learning objectives, participants will be well-equipped to excel in regression analysis tasks and make informed decisions using regression models.

Polynomial Regression

Regularization

Evaluation and Cross Validation

Ensemble Methods

Case Study