SKKU - Using R for Regression and Machine Learning in Investment
- Offered byCoursera
Using R for Regression and Machine Learning in Investment at Coursera Overview
Duration | 17 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Using R for Regression and Machine Learning in Investment at Coursera Highlights
- Flexible deadlines in accordance to your schedule.
- Earn a Certificate upon completion
Using R for Regression and Machine Learning in Investment at Coursera Course details
- In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning.
- The course is designed with the assumption that most students already have a little bit of knowledge in financial economics and R programming.
- The instructor will explain the detail of R programming. It will be an excellent course for you to improve your programming skills but you must have basic knowledge in R. If you are very good at R programming, it will provide you with an excellent opportunity to practice again with finance and investment examples.
Using R for Regression and Machine Learning in Investment at Coursera Curriculum
Understanding the big picture of the algorithm-driven investment decision-making process using machine learning and review of regression methodology
L1. Brief History on Investing, Machine Learning and Alternative Data
L2. Ingredients for Maching Learning Based Investment
L3. Big Picture of Alorithm-Driven Investment
L4. Understanding the Characteristics of Factors
L5. Understanding Machine Learning Concepts
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Regression and beyond
L6. Handling Data with Different Frequencies
L7. Analyzing Data Using Fama-Macbeth Regression
L8. Predictive Models
L9. Making a Model that Performs Well in Real Life
L10. Logistic Regression - Solving Classification Problems
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Macro factor model
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