Machine Learning Algorithms with R in Business Analytics
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- Duration 14 hours
- Course by University of Illinois Urbana-Champaign
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Offered by
About
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.Modules
Course Orientation
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3
Videos
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3
Readings
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1
Quiz
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1
Plugin
3
Videos
- Course Introduction
- About Professor Jessen Hobson
- About Professor Ronald Guymon
3
Readings
- Syllabus
- About the Discussion Forums
- Online Education at Gies College of Business
1
Quiz
- Orientation Quiz
About Your Classmates
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1
Readings
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1
Discussion
1
Discussions
- Getting to Know Your Classmates
1
Readings
- Updating Your Profile
Module 1 Information
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1
Videos
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2
Readings
1
Videos
- Module 1 Introduction
2
Readings
- Module 1 Overview
- Module 1 Readings
Why Isn't EDA Enough?
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1
Videos
1
Videos
- Why Isn't EDA Enough?
Business Problem
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3
Videos
3
Videos
- Business Problem
- Data
- What Problems Can Regression Answer?
Regression
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6
Videos
6
Videos
- Correlation
- Linear Models
- Simple Regression
- Residuals and Predictions
- Multiple Regression
- Dummy Variables
Module 1 Review
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1
Videos
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1
Quiz
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1
PeerReview
1
Peer Review
- Module 1 Peer Reviewed Assignment
1
Videos
- Module 1 Conclusion
1
Quiz
- Module 1 Quiz
Module 2 Information
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1
Videos
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2
Readings
1
Videos
- Module 2 Introduction
2
Readings
- Module 2 Overview
- Module 2 Reading
Machine Learning Overview
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5
Videos
5
Videos
- Inference, Prediction, and Experimentation
- Categories of ML Models, Part 1, Types of Data and Terms
- Categories of ML Models, Part 2, Categories of Algos
- How Machine Learning Works in General
- Evaluating ML Model Quality
Business Problem
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2
Videos
2
Videos
- Introduce the Business Problem to Solve
- Introduce the Data
Logistic Regression
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5
Videos
5
Videos
- Introduction to Logistic Regression
- Logistic Regression Hands on - One Variable (Part1)
- Logistic Regression Hands on - One Variable (Part2)
- Logistic Regression Hands on - One Variable (Part3)
- Logistic Regression Hands on - Multiple Variables
Module 2 Review
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1
Videos
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1
Quiz
1
Videos
- Module 2 Conclusion
1
Quiz
- Module 2 Quiz
Module 3 Information
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1
Videos
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2
Readings
1
Videos
- Module 3 Introduction
2
Readings
- Module 3 Overview
- Module 3 Reading
Business Problem
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2
Videos
2
Videos
- Introduce the Data
- Introduction to Classification
K-Nearest Neighbors
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5
Videos
5
Videos
- Introduce the Business Problem
- Introduction to K-Nearest Neighbors
- Creating KNN Model (Part 1)
- Creating KNN Model (Part 2)
- Creating KNN Model (Part 3)
Decision Trees
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3
Videos
3
Videos
- Introduction to Decision Trees
- Creating Decision Tree Models
- Evaluating Results from Decision Trees
Module 3 Review
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1
Videos
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1
Quiz
1
Videos
- Module 3 Conclusion
1
Quiz
- Module 3 Quiz
Module 4 Information
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1
Videos
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2
Readings
1
Videos
- Module 4 Introduction
2
Readings
- Module 4 Overview
- Module 4 Reading
Business Problem
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3
Videos
3
Videos
- Introduce the Data
- Introduction to Clustering and Questions that Clustering Can Answer
- Introduce the Business Problem
K-Means Clustering
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3
Videos
3
Videos
- Introduction to K-Means Clustering
- Creating K-Means Clusters
- Evaluating K-Means Clusters
Density-Based Clustering
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3
Videos
3
Videos
- Introduction to Density-Based Clustering
- Creating Density-Based Clusters
- Evaluating Density-Based Clusters
Module 4 Review
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1
Videos
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1
Quiz
1
Videos
- Module 4 Conclusion
1
Quiz
- Module 4 Quiz
Course Conclusion
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2
Videos
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2
Readings
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1
Discussion
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1
Plugin
1
Discussions
- Reflection
2
Videos
- Course Conclusion
- Learn on Your Terms
2
Readings
- Congratulations on completing the course!
- Get Your Course Certificate
Auto Summary
Discover the power of machine learning algorithms in business analytics with this professional-level course from Coursera. Dive into the conceptual foundations and practical applications of predictive and classification algorithms, and learn to create meaningful data groups. Enhance your skills using R and RStudio, and effectively communicate your findings through R notebooks. Ideal for business and management professionals, the course spans 840 minutes and offers Starter and Professional subscription options. Join now to unlock actionable insights from your data.
Ronald Guymon
Gies College of Business, University of Illinois