课程

Linear Classifiers in Python

中级技能水平

更新时间 2023年10月

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

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PythonMachine Learning4 小时13 视频44 练习3,200 经验值65,652成就声明

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课程描述

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.

先决条件

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

50 经验值

KNN classification

100 经验值

Comparing models

50 经验值

Overfitting

50 经验值

Applying logistic regression and SVM

50 经验值

Running LogisticRegression and SVC

100 经验值

Sentiment analysis for movie reviews

100 经验值

Linear classifiers

50 经验值

Which decision boundary is linear?

50 经验值

Visualizing decision boundaries

100 经验值

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

50 经验值

How models make predictions

50 经验值

Changing the model coefficients

100 经验值

What is a loss function?

50 经验值

The 0-1 loss

50 经验值

Minimizing a loss function

100 经验值

Loss function diagrams

50 经验值

Classification loss functions

50 经验值

Comparing the logistic and hinge losses

100 经验值

Implementing logistic regression

100 经验值

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

50 经验值

Regularized logistic regression

100 经验值

Logistic regression and feature selection

100 经验值

Identifying the most positive and negative words

100 经验值

Logistic regression and probabilities

50 经验值

Getting class probabilities

50 经验值

Regularization and probabilities

100 经验值

Visualizing easy and difficult examples

100 经验值

Multi-class logistic regression

50 经验值

Counting the coefficients

50 经验值

Fitting multi-class logistic regression

100 经验值

Visualizing multi-class logistic regression

100 经验值

One-vs-rest SVM

100 经验值

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

50 经验值

Support vector definition

50 经验值

Effect of removing examples

100 经验值

Kernel SVMs

50 经验值

GridSearchCV warm-up

100 经验值

Jointly tuning gamma and C with GridSearchCV

100 经验值

Comparing logistic regression and SVM (and beyond)

50 经验值

An advantage of SVMs

50 经验值

An advantage of logistic regression

50 经验值

Using SGDClassifier

100 经验值

50 经验值

Linear Classifiers in Python

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