1 Learning Objectives

  • The basics of machine learning
  • How to perform cross-validation to avoid overtraining
  • Several popular machine learning algorithms
  • How to build a recommendation system
  • What regularization is and why it is useful

1.1 Course Overview

There are six major sections in this course: introduction to machine learning; machine learning basics; linear regression for prediction, smoothing, and working with matrices; distance, knn, cross validation, and generative models; classification with more than two classes and the caret package; and model fitting and recommendation systems.

1.1.1 Introduction to Machine Learning

In this section, you’ll be introduced to some of the terminology and concepts you’ll need going forward.

1.1.2 Machine Learning Basics

In this section, you’ll learn how to start building a machine learning algorithm using training and test data sets and the importance of conditional probabilities for machine learning.

1.1.3 Linear Regression for Prediction, Smoothing, and Working with Matrices

In this section, you’ll learn why linear regression is a useful baseline approach but is often insufficiently flexible for more complex analyses, how to smooth noisy data, and how to use matrices for machine learning.

1.1.4 Distance, Knn, Cross Validation, and Generative Models

In this section, you’ll learn different types of discriminative and generative approaches for machine learning algorithms.

1.1.5 Classification with More than Two Classes and the Caret Package

In this section, you’ll learn how to overcome the curse of dimensionality using methods that adapt to higher dimensions and how to use the caret package to implement many different machine learning algorithms.

1.1.6 Model Fitting and Recommendation Systems

In this section, you’ll learn how to apply the machine learning algorithms you have learned.