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. 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.
Machine Learning Algorithms with R in Business Analytics
This course is part of Business Analytics Specialization
Instructors: Ronald Guymon
Sponsored by Syrian Youth Assembly
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What you'll learn
1. Conceptual framework of ML algorithms
2. Conceptual foundation for interpreting ML results
3. Practice applying ML algorithms to business data
Skills you'll gain
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There are 4 modules in this course
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.
What's included
15 videos6 readings2 quizzes1 peer review1 discussion prompt1 plugin
Gain an understanding of machine learning in business and logistic regression
What's included
14 videos2 readings1 quiz
Classification algorithms in general, K-nearest neighbors, and decision trees.
What's included
12 videos2 readings1 quiz
Clustering algorithms, k-means, and DBSCAN
What's included
13 videos4 readings1 quiz1 discussion prompt1 plugin
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