Introduction to Machine Learning: Supervised Learning
Master Python-based machine learning foundations, from regression to support vector machines. Ideal for intermediate programmers.
Master Python-based machine learning foundations, from regression to support vector machines. Ideal for intermediate programmers.
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 Machine Learning: Theory and Hands-on Practice 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.
3.3
(55 ratings)
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What you'll learn
Use modern machine learning tools and Python libraries effectively
Implement and evaluate logistic regression models
Handle linearly-inseparable data with advanced techniques
Design and optimize decision tree algorithms
Apply ensemble methods for improved model performance
Master support vector machines and kernel methods
Skills you'll gain
This course includes:
5.7 Hours PreRecorded video
9 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course covers fundamental supervised machine learning concepts and their practical implementation using Python. Students learn various algorithms including linear regression, logistic regression, decision trees, and support vector machines. The curriculum emphasizes both theoretical understanding and hands-on practice, with extensive programming assignments using popular libraries like scikit-learn. Topics include model evaluation, hyperparameter tuning, and ensemble methods, providing a solid foundation for real-world machine learning applications.
Introduction to Machine Learning, Linear Regression
Module 1 · 7 Hours to complete
Multilinear Regression
Module 2 · 6 Hours to complete
Logistic Regression
Module 3 · 6 Hours to complete
Non-parametric Models
Module 4 · 6 Hours to complete
Ensemble Methods
Module 5 · 6 Hours to complete
Kernel Method
Module 6 · 7 Hours to complete
Fee Structure
Instructor
Adjunct Professor
Dr. Geena Kim is an Adjunct Professor in the Computer Science Department at the University of Colorado Boulder, where she specializes in deep learning and machine learning. She holds a Ph.D. from UC Berkeley and has extensive experience in both academia and industry, currently serving as a Research Scientist at Amazon. Her career also includes entrepreneurial ventures and technical advisory roles for Internet of Things (IoT) startups in the Bay Area, showcasing her versatile expertise in cutting-edge technology.Dr. Kim teaches several courses that focus on machine learning techniques, including "Introduction to Deep Learning," "Introduction to Machine Learning: Supervised Learning," and "Unsupervised Algorithms in Machine Learning." Her research interests encompass deep learning, computer vision, and medical image analysis, contributing to advancements in these fields through innovative applications. With a strong commitment to education and research, Geena Kim continues to influence the next generation of computer scientists and data analysts at CU Boulder.
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3.3 course rating
55 ratings
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