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.
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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
- Conceptual framework of ML algorithms
- Conceptual foundation for interpreting ML results
- 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
