Supervised Machine Learning: Classification
Master classification algorithms and predictive modeling with hands-on practice in Python. Perfect for aspiring data scientists seeking practical ML skills.
Master classification algorithms and predictive modeling with hands-on practice in Python. Perfect for aspiring data scientists seeking practical ML skills.
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.
4.8
(342 ratings)
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English
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What you'll learn
Differentiate uses and applications of classification and classification ensembles
Implement logistic regression models for predictive analysis
Master decision tree and tree-ensemble models
Apply various ensemble methods for classification
Use error metrics to select optimal classification models
Handle unbalanced classes using oversampling and undersampling techniques
Skills you'll gain
This course includes:
8.5 Hours PreRecorded video
22 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This comprehensive course focuses on classification techniques in supervised machine learning. Students learn to train predictive models for categorical outcomes, covering logistic regression, decision trees, support vector machines, and ensemble methods. The curriculum emphasizes practical implementation using Python, including handling unbalanced datasets and evaluating model performance through various error metrics. Through hands-on labs and real-world examples, learners develop skills in implementing and optimizing different classification algorithms.
Logistic Regression
Module 1 · 3 Hours to complete
K Nearest Neighbors
Module 2 · 2 Hours to complete
Support Vector Machines
Module 3 · 2 Hours to complete
Decision Trees
Module 4 · 2 Hours to complete
Ensemble Models
Module 5 · 8 Hours to complete
Modeling Unbalanced Classes
Module 6 · 5 Hours to complete
Fee Structure
Instructors
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
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.
Testimonials
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4.8 course rating
342 ratings
Frequently asked questions
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