scikit-learn: Fundamentals of Support Vector Machines

In this course, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). You will then apply this knowledge to build SVMs for your own classification tasks. A basic familiarity of Python is assumed.

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scikit-learn: Fundamentals of Support Vector Machines

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Task List


We will cover the following tasks in 1 hour and 40 minutes:


Getting Started

In this task, you will get a sense of your virtual machine, the Jupyter Notebook software, and the course material.


Beyond Linear Discriminative Classifiers

Understand the scope of scope and limitations of linear classifiers, and how SVMs offer a way to overcome them. We will use Scikit-Learn to generate a random dataset with two linearly separable classes. Next we will try finding the best decision boundary for our data.


Many Possible Separators

In this chapter, we will plot multiple decision boundaries that give us perfect in-sample classification. We learn why these linear models lead to poor generalization performance and how SVMs provide a way to overcome them.


Plotting the Margins

In this chapter, we will plot margins around our three decision boundaries. We will also look at some of the mathematics and frame our constrained optimization problem in the language of quadratic programming.


Training an SVM Model

In this chapter, we will train an SVM model with Scikit-Learn’s support vector classifier (SVC) and fit the model to our data.


Facial Recognition with SVMs

As our first application of SVMs to real-world tasks, we begin to tackle the domain of facial recognition. In this task, we load the Labeled Faces data from the Wild dataset. Built into Scikit-Learn, this dataset consists of thousands of labeled photographs of various public figures.


Exploring the data set

We plot some of the faces from our data to get a sense of what we are working with.


Preprocessing the data set

In the previous task we observed that each image consists of nearly 3000 pixel values. Instead of simply using each one as a feature, we use Principal Component Analysis to extract more meaningful features which we will feed to our Support Vector Classifier.

After preprocessing our data, we split them into training and test sets.


Grid-Search Cross Validation

In this task, we will determine the best model. To do so, we will use grid search cross-validation to determine the optimal parameters from all the possible combinations.


Visualize Test Images

With our cross-validated model, we plot a few of the test images with their predicted labels. Remember, our model predicts labels for data that it hasn’t encountered previously during the training process.


Evaluating the Support Vector Classifier

We evaluate our classifier’s out-of-sample performance and get the recovery statistics using the classification report. We also visualize the confusion matrix to understand which labels might be confusing our classifier.

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Snehan Kekre

About the Host (Snehan Kekre)


Snehan hosts Machine Learning courses at Rhyme. He is in his senior year of university at the Minerva Schools at KGI, pursuing a double major in the Natural Sciences and Computational Sciences, with a focus on physics and machine learning. When not applying computational and quantitative methods to identify the structures shaping the world around him, he can sometimes be seen trekking in the mountains of Nepal.



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