Course Overview

The course is divided into six modules based on the modeling families of supervised machine learning. Students will learn how to train regression models to predict outcomes and how to use error metrics to compare various models. Moreover, the course helps students walk through the best practices related to machine learning.

After completing the course, students will be able to use and differentiate between regression applications, use linear regression models, use various error metrics, and select the model that suits the data. They will also be able to articulate the regularization that will prevent overfitting, and finally, they can use regressions such as Ridge, Lasso, and elastic net.

• Linear regression
• Machine learning algorithms
• Supervised learning
• Regression analysis

Requirements

• Basic knowledge of Python and data cleaning
• A computer or laptop with internet connectivity.
• Learn about techniques of regression in approximately 20 hours.
Flexible work schedule.

Accessto Career Resources with Supervised Machine Learning: Regression Course

Enroll Free

Enhanced Employability:

An outstanding Supervised Machine Learning: Regression Program that increases your chances of landing the best jobs.

Competitive Advantage:

An opportunity to stand out from the competition and make a more significant impression on potential employers.

Skill Validation:

Robust validation of regression skills confirms that you can complete all related tasks.

Networking Opportunities:

Managing Supervised Machine Learning: Regression courses provide ample networking opportunities for participants. Learners can connect with classmates and industry professionals through cooperative exercises, group discussions, and interactive seminars.