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Course

Linear Classifiers in Python

IntermediateSkill Level

4.8+

Updated 10/2023

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

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PythonMachine Learning4 hr13 videos44 Exercises3,200 XP65,668Statement 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

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.

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

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.

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

In this chapter you will delve into the details of logistic regression. You'll learn all about regularization and how to interpret model output.

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

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.

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

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FAQs

What machine learning algorithms does this course focus on?

You will learn logistic regression and support vector machines (SVMs), including how to train, test, and tune both classifiers using scikit-learn.

What prior knowledge do I need?

This is an intermediate course. You should know pandas, introductory statistics, and have completed Supervised Learning with scikit-learn before starting.

Does this course cover the theory behind the classifiers?

Yes. You will learn about loss functions, regularization, and the conceptual framework that explains how logistic regression and SVMs make classification decisions.

How will this course help me understand other ML algorithms?

Linear classifiers provide a conceptual foundation for understanding many other machine learning models, since loss functions and regularization appear throughout the field.

How is the course structured?

It has 4 chapters and 45 exercises covering applied classification, loss functions, logistic regression details, and SVM details. Most learners finish in about 2.5 hours.

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