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The module provides an introductory overview of the course and introduces the course instructor.

Throughout the week, you will learn how to apply SVMs to classify or predict outcomes in a given dataset, select appropriate kernel functions and parameters, and evaluate model performance.

In this module, we will cover introductory concepts of neural networks, such as activation functions and backpropagation. You will have the opportunity to apply Neural Networks to classify or predict outcomes in a given dataset and evaluate model performance in the labs for this module.

This module will focus on the ensemble methods decision trees, bagging, and random forests, which combine multiple models to improve prediction accuracy and reduce overfitting. Decision Trees are a popular machine learning method that partitions the feature space into smaller regions and models the response variable in each region using simple rules. However, Decision Trees can suffer from high variance and instability, which can be addressed by Bagging and Random Forests. Bagging involves generating multiple trees on bootstrapped samples of the data and averaging their predictions, while Random Forests further decorrelate the trees by randomly selecting subsets of features for each tree.

You will complete a programming assignment and multiple choice exam worth 25% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.