Classification Analysis
Master supervised learning techniques with hands-on practice in classification methods using Python for data analysis.
Master supervised learning techniques with hands-on practice in classification methods using Python for data analysis.
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 Data Analysis with Python Specialization 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.
Instructors:
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What you'll learn
Understand and apply various classification algorithms to real datasets
Evaluate classifier performance using multiple metrics
Select appropriate classification methods for different problems
Implement binary and multiclass classification tasks
Tune and optimize classifiers for improved performance
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
6 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores classification techniques in supervised learning. Students learn various classification algorithms including K-Nearest Neighbors (KNN), Decision Trees, Support Vector Machines (SVM), Naive Bayes, and Logistic Regression. The curriculum covers both theoretical foundations and practical applications through hands-on tutorials and real-world case studies. Participants learn to evaluate classifier performance using metrics like accuracy, precision, recall, and ROC curves, gaining expertise in selecting and fine-tuning appropriate classifiers for different scenarios.
Introduction to Classification
Module 1 · 7 Hours to complete
Decision Tree Classification
Module 2 · 6 Hours to complete
Support Vector Machine Classification
Module 3 · 6 Hours to complete
Naïve Bayes and Logistic Regression
Module 4 · 9 Hours to complete
Classification Evaluation
Module 5 · 48 Minutes to complete
Case Study
Module 6 · 7 Hours to complete
Fee Structure
Instructor
Teaching Assistant Professor
Dr. Di Wu is a Teaching Assistant Professor at the University of Colorado Boulder, specializing in data science and computer science. His primary research interests include temporal databases, the semantic web, knowledge representation, and data science, with a focus on extending the Resource Description Framework (RDF) for temporal dimensions. Before joining CU Boulder, he taught various courses such as algorithms and data structures, programming languages, and database management. Dr. Wu aims to develop an inclusive and engaging pedagogy in data science education over the next five years, emphasizing experiential learning in both in-person and online formats. He is involved in teaching courses related to data science and programming, including specializations in Python programming for data scientists.
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