Intro to Machine Learning

Meet with Sebastian and Katie to discuss machine learning.

Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.

Build an intuition about how support vector machines (SVMs) work and implement one using scikit-learn.

Learn about how the decision tree algorithm works, including the concepts of entropy and information gain.

In this mini project, you will extend your toolbox of algorithms by choosing your own algorithm to classify terrain data, including k-nearest neighbors, AdaBoost, and random forests.

Find out about the Enron data set used in the next lessons and mini-projects.

See how we can model continuous data using linear regression.

Sebastian discusses outlier detection and removal.

Learn about what unsupervised learning is and find out how to use scikit-learn's k-means algorithm.

Learn about feature rescaling and find out which algorithms require feature rescaling before use.

Katie discusses when and why to use feature selection, and provides some methods for doing this.

Find out how to use text data in your machine learning algorithm.

Learn about data dimensionality and reducing the number of dimensions with principal component analysis (PCA).

Learn more about testing, training, cross validation, and parameter grid searches in this lesson.

How do we know if our classifier is performing well? Katie discusses different evaluation metrics for classifiers in this lesson.

Spend some time reflecting on the course material with Sebastian and Katie!