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Linear Classifiers in Python

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

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4 Hours13 Videos44 Exercises40,860 Learners3200 XPMachine Learning Fundamentals TrackMachine Learning Scientist Track

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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.
  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.

    Play Chapter Now
    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.

    Play Chapter Now
    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. 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. 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 FundamentalsMachine Learning Scientist

Collaborators

nicksolomon
Nick Solomon
karawoo
Kara Woo

Prerequisites

Supervised Learning with scikit-learn
Mike Gelbart Headshot

Mike Gelbart

Instructor, the University of British Columbia

Mike Gelbart is an Instructor in the Department of Computer Science at the University of British Columbia (UBC) in Vancouver, Canada. He also teaches in, and co-designed, the Master of Data Science program at UBC. Mike received his undergraduate degree in physics from Princeton University and his PhD from the machine learning group at Harvard University, working on hyperparameter optimization for machine learning.
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