Supervised Machine Learning: Regression
Master regression techniques in machine learning using Python. Learn linear, polynomial, and regularized regression for predictive modeling.
Master regression techniques in machine learning using Python. Learn linear, polynomial, and regularized regression for predictive modeling.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full IBM Machine Learning Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
(557 ratings)
43,573 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
Powered by
What you'll learn
Differentiate between classification and regression in supervised machine learning
Implement and optimize linear regression models
Use various error metrics to evaluate regression models
Understand and apply regularization techniques
Master Ridge, LASSO, and Elastic Net regression methods
Implement cross-validation and data splitting strategies
Skills you'll gain
This course includes:
7 Hours PreRecorded video
13 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 6 modules in this course
This comprehensive course focuses on regression techniques in supervised machine learning. Students learn to train models for continuous outcome prediction, covering linear regression, polynomial regression, and regularization methods. The curriculum emphasizes practical implementation using Python, including data splitting strategies, cross-validation, and model selection. Through hands-on labs and real-world examples, learners develop skills in implementing and optimizing different regression algorithms while understanding the bias-variance trade-off.
Introduction to Supervised Machine Learning and Linear Regression
Module 1 · 3 Hours to complete
Data Splits and Polynomial Regression
Module 2 · 3 Hours to complete
Cross Validation
Module 3 · 3 Hours to complete
Bias Variance Trade off and Regularization Techniques
Module 4 · 3 Hours to complete
Regularization Details
Module 5 · 3 Hours to complete
Final Project
Module 6 · 2 Hours to complete
Fee Structure
Instructors
Digital Content Delivery Lead at IBM with Extensive Experience in Information Technology Education
Mark J. Grover is a Digital Content Delivery Lead at IBM, specializing in the creation and delivery of online educational content. Before joining IBM, he was a full-time professor of computer technology at Cape Fear Community College in Wilmington, NC, where he coordinated the Information Security program and taught various courses including Computer Security and Network Administration. Grover has over 25 years of experience in information technology and has received accolades such as the Cisco Instructor of Excellence award and the Award for Excellence in Innovation from the University of North Carolina Wilmington. He is passionate about outdoor activities like camping and mountain biking, and enjoys spending time with his family.
Machine Learning Curriculum Developer at IBM Specializing in Data Analysis and AI Education
Miguel Maldonado is a Machine Learning Curriculum Developer at IBM, where he specializes in creating educational content focused on machine learning and data analysis. He teaches several courses on Coursera, including Deep Learning and Reinforcement Learning, Specialized Models: Time Series and Survival Analysis, Supervised Machine Learning: Classification, Supervised Machine Learning: Regression, and Unsupervised Machine Learning. Through his work, Miguel aims to equip learners with the essential skills needed to understand and apply various machine learning techniques across different domains, helping to bridge the gap between theoretical knowledge and practical application in the field of artificial intelligence.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.7 course rating
557 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.