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Linear Classifiers in Python

Intermediate

4.0+

Updated 12/2024

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

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PythonMachine Learning4 hours13 videos44 exercises3,200 XP57,155Statement of Accomplishment

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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.

Prerequisites

Supervised Learning with scikit-learn

1

Applying logistic regression and SVM

scikit-learn refresher

KNN classification

Comparing models

Overfitting

Applying logistic regression and SVM

Running LogisticRegression and SVC

Sentiment analysis for movie reviews

Linear classifiers

Which decision boundary is linear?

Visualizing decision boundaries

2

Loss functions

Linear classifiers: the coefficients

How models make predictions

Changing the model coefficients

What is a loss function?

The 0-1 loss

Minimizing a loss function

Loss function diagrams

Classification loss functions

Comparing the logistic and hinge losses

Implementing logistic regression

3

Logistic regression

Logistic regression and regularization

Regularized logistic regression

Logistic regression and feature selection

Identifying the most positive and negative words

Logistic regression and probabilities

Getting class probabilities

Regularization and probabilities

Visualizing easy and difficult examples

Multi-class logistic regression

Counting the coefficients

Fitting multi-class logistic regression

Visualizing multi-class logistic regression

One-vs-rest SVM

4

Support Vector Machines

Support vectors

Support vector definition

Effect of removing examples

Kernel SVMs

GridSearchCV warm-up

Jointly tuning gamma and C with GridSearchCV

Comparing logistic regression and SVM (and beyond)

An advantage of SVMs

An advantage of logistic regression

Using SGDClassifier

Linear Classifiers in Python

Course
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Don’t just take our word for it

*4.0

from 31 reviews

61%

6%

19%

6%

6%

Highest to Lowest
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Most recent
Top reviews

Shing L.

5 months

More material on the background on regulation and different hyperparameter is beneficial

Li D.

6 months

Great course

YUN S.

8 months

Well organized to understand the Linear Classifiers with classification and regress models. I might be better to move multi-class logistic regression to the last chapter after the SVM chapter

Sue D.

10 months

The course is fascinating, and the instructor is stunning!

Alison N.

about 1 year

None

"More material on the background on regulation and different hyperparameter is beneficial"

Shing L.

"Great course"

Li D.

"Well organized to understand the Linear Classifiers with classification and regress models. I might be better to move multi-class logistic regression to the last chapter after the SVM chapter"

YUN S.

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