Machine Learning: Concepts and Applications

You will learn about two more classification techniques in this module: first, Support Vector Machines (SVMs) and then Naive Bayes, a quick and highly interpretable approach that uses Bayes' theorem.

In this module, you will first learn about classification using decision trees. We will see how to create models that use individual decision trees, and then ensemble models, which use many trees, such as bagging, boosting, and random forests. Then, we learn more about how to evaluate the performance of classifiers.

To this point, we have been focusing on supervised learning and training models that estimate a target variable that you have specified. In this module, we take our first look at unsupervised learning, a domain of machine learning that uses techniques to find patterns and relationships in data without you ever defining a target. In particular, we look at a variety of clustering techniques, beginning with k-means and hierarchical clustering, and then distribution and density-based clustering.

You will look at two new techniques in this module. The first is Principal Component Analysis, a powerful dimensionality reduction technique that you can use to project high-dimensional features into lower-dimensional spaces. This can be used for a range of purposes, including feature selection, preventing overfitting, visualizing in two- or three-dimensional spaces higher dimensional data, and more. Then, you will study hidden Markov models, a technique that you can use to model sequences of states, where each state depends on the one that came before.

This module introduces you to one of the most hyped topics in machine learning, deep learning with feed-forward neural networks and convolutional neural networks. You will learn about how these techniques work and where they might be very effective--or very ineffective. We explore how to design, implement, and evaluate such models using Python and Keras.