UIUC - Machine Learning Algorithms with R in Business Analytics
- Offered byCoursera
Machine Learning Algorithms with R in Business Analytics at Coursera Overview
Duration | 14 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Algorithms with R in Business Analytics at Coursera Highlights
- Reset deadlines in accordance to your schedule.
- Shareable Certificate
- Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
Experience using R to assemble data, summarize data, and visually explore data.
Machine Learning Algorithms with R in Business Analytics at Coursera Course details
- This programe 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 at Coursera Curriculum
Course Orientation and Module 1: Regression Algorithm for Testing and Predicting Business Data
Course Introduction
About Professor Jessen Hobson
About Professor Ronald Guymon
Learn on Your Terms
Module 1 Introduction
Why Isn't EDA Enough?
Business Problem
Data
What Problems Can Regression Answer?
Correlation
Linear Models
Simple Regression
Residuals and Predictions
Multiple Regression
Dummy Variables
Module 1 Conclusion
Syllabus
About the Discussion Forums
Learn More About Flexible Learning Paths
Module 1 Overview
Module 1 Readings
Orientation Quiz
Module 1 Quiz
Module 2: Framework for Machine Learning and Logistic Regression
Module 2 Introduction
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
Introduce the Business Problem to Solve
Introduce the Data
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 Conclusion
Module 2 Overview
Module 2 Reading
Module 2 Quiz
Module 3: Classification Algorithms
Module 3 Introduction
Introduce the Data
Introduction to Classification
Introduce the Business Problem
Introduction to K-Nearest Neighbors
Creating KNN Model (Part 1)
Creating KNN Model (Part 2)
Creating KNN Model (Part 3)
Introduction to Decision Trees
Creating Decision Tree Models
Evaluating Results from Decision Trees
Module 3 Conclusion
Module 3 Overview
Module 3 Reading
Module 3 Quiz
Module 4: Clustering Algorithms
Module 4 Introduction
Introduce the Data
Introduction to Clustering and Questions that Clustering Can Answer
Introduce the Business Problem
Introduction to K-Means Clustering
Creating K-Means Clusters
Evaluating K-Means Clusters
Introduction to Density-Based Clustering
Creating Density-Based Clusters
Evaluating Density-Based Clusters
Module 4 Conclusion
Course Conclusion
Gies Online Programs
Module 4 Overview
Module 4 Reading
Congratulations
Get Your Course Certificate
Module 4 Quiz
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