Popular Courses
Intro to Python
Intro to R
Intro to SQL
Popular Searches
SQL
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
- 9,620 Participants
- 3,200 XP
Create Your Free Account
By pressing Continue you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.
Loved by learners at thousands of top companies:
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.
-
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. -
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.
-
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.
-
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.
-
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.
-
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.
-
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.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
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 MoreTracks
Collaborators
-
Nick Solomon
-
Kara Woo