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Imran Ihsan has completed

Linear Classifiers in Python

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4 hours

3,200 XP

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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
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
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.
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Linear classifiers: the coefficients
50 xp
How models make predictions
50 xp
Changing the model coefficients
100 xp
What is a loss function?
50 xp
The 0-1 loss
50 xp
Minimizing a loss function
100 xp
Loss function diagrams
50 xp
Classification loss functions
50 xp
Comparing the logistic and hinge losses
100 xp
Implementing logistic regression
100 xp
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.
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Logistic regression and regularization
50 xp
Regularized logistic regression
100 xp
Logistic regression and feature selection
100 xp
Identifying the most positive and negative words
100 xp
Logistic regression and probabilities
50 xp
Getting class probabilities
50 xp
Regularization and probabilities
100 xp
Visualizing easy and difficult examples
100 xp
Multi-class logistic regression
50 xp
Counting the coefficients
50 xp
Fitting multi-class logistic regression
100 xp
Visualizing multi-class logistic regression
100 xp
One-vs-rest SVM
100 xp
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.
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Support vectors
50 xp
Support vector definition
50 xp
Effect of removing examples
100 xp
Kernel SVMs
50 xp
GridSearchCV warm-up
100 xp
Jointly tuning gamma and C with GridSearchCV
100 xp
Comparing logistic regression and SVM (and beyond)
50 xp
An advantage of SVMs
50 xp
An advantage of logistic regression
50 xp
Using SGDClassifier
100 xp
Conclusion
50 xp

In the following tracks

Machine Learning Fundamentals Machine Learning Scientist Supervised Machine Learning

Collaborators

Nick Solomon

Kara Woo

Prerequisites

Supervised Learning with scikit-learn

Instructor, the University of British Columbia

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