Linear Classifiers in Python
In this course you will learn the details of linear classifiers like logistic regression and SVM.
- 4 hours
- 13 Videos
- 44 Exercises
- 4,495 Participants
- 3,200 XP
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This course is part of these tracks:
Mike Gelbart
Instructor, the University of British Columbia
Mike Gelbart is an Instructor in the Department of Computer Science at the University of British Columbia (UBC) in Vancouver, Canada. He also teaches in, and co-designed, the Master of Data Science program at UBC. Mike received his undergraduate degree in physics from Princeton University and his PhD from the machine learning group at Harvard University, working on hyperparameter optimization for machine learning.
See MoreCollaborator(s)
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Nick Solomon
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Kara Woo
Prerequisites
Course Description
In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn. Once you've learned how to apply these methods, you'll dive into the ideas behind them and find out what really makes them tick. At the end of this course you'll know how to train, test, and tune these linear classifiers in Python. You'll also have a conceptual foundation for understanding many other machine learning algorithms.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Course Outline
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1
Applying logistic regression and SVM
FreeIn this chapter you will learn the basics of applying logistic regression and support vector machines (SVMs) to classification problems. You'll use the
scikit-learnlibrary to fit classification models to real data. -
Loss functions
In this chapter you will discover the conceptual framework behind logistic regression and SVMs. This will let you delve deeper into the inner workings of these models.
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Logistic regression
In this chapter you will delve into the details of logistic regression. You'll learn all about regularization and how to interpret model output.
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Support Vector Machines
In this chapter you will learn all about the details of support vector machines. You'll learn about tuning hyperparameters for these models and using kernels to fit non-linear decision boundaries.
Course Instructor
Mike Gelbart
Instructor, the University of British Columbia
Mike Gelbart is an Instructor in the Department of Computer Science at the University of British Columbia (UBC) in Vancouver, Canada. He also teaches in, and co-designed, the Master of Data Science program at UBC. Mike received his undergraduate degree in physics from Princeton University and his PhD from the machine learning group at Harvard University, working on hyperparameter optimization for machine learning.
See MoreCollaborator(s)
-
Nick Solomon
-
Kara Woo
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