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Linear Classifiers in Python

4.1+
30 reviews
Intermediate

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

4 hours13 videos44 exercises

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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.

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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

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Supervised Machine Learning in Python

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collaborators

audio recorded by

prerequisites

Supervised Learning with scikit-learn
Mike Gelbart

Instructor, the University of British Columbia

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Don’t just take our word for it

*4.1
from 30 reviews
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• Shing L.

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

• Li D.
2 months

Great course

• YUN S.
5 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.
7 months

The course is fascinating, and the instructor is stunning!

• Alison N.
10 months

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.