One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems.

Enroll in course

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use R and RStudio to run these algorithms and communicate the results using R notebooks.

What You Will Learn

1. Conceptual framework of ML algorithms

2. Conceptual foundation for interpreting ML results

3. Practice applying ML algorithms to business data

Syllabus

WEEK 1

Course Orientation and Module 1: Regression Algorithm for Testing and Predicting Business Data

Exploratory data analysis (EDA) is a critical step in the business analytic workflow; however, EDA is a time-consuming approach for uncovering complex relationships. Moreover, the visualizations that are often used for EDA do not lend themselves well for quantifying confidence in results or for making predictions.

WEEK 2

Framework for Machine Learning and Logistic Regression

Gain an understanding of machine learning in business and logistic regression

WEEK 3

Classification Algorithms

Classification algorithms in general, K-nearest neighbors, and decision trees.

WEEK 4

Clustering Algorithms

Clustering algorithms, k-means, and DBSCAN