# Linear Classifiers in Python

4.1+
26 reviews
Intermediate

In this course you will learn the details of linear classifiers like logistic regression and SVM.

4 Hours13 Videos44 Exercises

or

## 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. 1

### Applying logistic regression and SVM

Free

In this chapter you will learn the basics of applying logistic regression and support vector machines (SVMs) to classification problems. You'll use the `scikit-learn` library to fit classification models to real data.

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scikit-learn refresher
50 xp
KNN classification
100 xp
Comparing models
50 xp
Overfitting
50 xp
Applying logistic regression and SVM
50 xp
Running LogisticRegression and SVC
100 xp
Sentiment analysis for movie reviews
100 xp
Linear classifiers
50 xp
Which decision boundary is linear?
50 xp
Visualizing decision boundaries
100 xp
2. 2

### 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.

3. 3

### 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.

4. 4

### 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.

In the following tracks

Machine Learning Fundamentals with PythonMachine Learning Scientist with PythonSupervised Machine Learning in Python

Collaborators

Audio Recorded By

Mike Gelbart

Instructor, the University of British Columbia

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## Don’t just take our word for it

*4.1
from 26 reviews
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• Edwin A.
5 months

I thought this course is a recommended course for those who want to learn about linear classifiers in Python.

• David T.
9 months

Course is great. It is easy to understand

• Andrew G.
10 months

Really good course.

• Kayleigh W.
10 months

Good!

• Isaac H.
11 months

Professor is incredibly clear. Concepts are explained in a simple and efficient way. Overall, one of the best courses I have taken on datacamp!

"I thought this course is a recommended course for those who want to learn about linear classifiers in Python."

Edwin A.

"Course is great. It is easy to understand"

David T.

"Really good course."

Andrew G.