SKKU - R for Regression and Machine Learning in Investment
- Offered byFutureLearn
R for Regression and Machine Learning in Investment at FutureLearn Overview
R for Regression and Machine Learning in Investment
at FutureLearn
Duration | 2 weeks |
Total fee | ₹2,205 |
Mode of learning | Online |
Credential | Certificate |
R for Regression and Machine Learning in Investment at FutureLearn Highlights
R for Regression and Machine Learning in Investment
at FutureLearn
- Earn a certificate upon completion
R for Regression and Machine Learning in Investment at FutureLearn Course details
R for Regression and Machine Learning in Investment
at FutureLearn
Skills you will learn
More about this course
- This two-week course from Sungkyunkwan University (SKKU) will introduce you to fundamental machine learning and regression methodologies and help you improve your R programming skills to use data analysis in solving investment problems
- This course will guide you through using regression methodology for various investment analysis purposes by using Ridge, Lasso, and logistic regression
- You’ll begin by gauging investment strategy using backtesting and learn about regression methodology, how to solve classification problems with logistic regression, and analyse data using the Fama-Macbeth Regression method
- On this course, you’ll take the first step toward using machine learning methodologies in solving investment problems
R for Regression and Machine Learning in Investment at FutureLearn Curriculum
R for Regression and Machine Learning in Investment
at FutureLearn
Understanding algorithm-driven investment decision-making.
Welcome to the course!
Brief History on Investing, Machine Learning and Alternative Data.
Ingredients for Maching Learning Based Investment
Big Picture of Algorithm-Driven Investment.
Understanding the Characteristics of Factors.
Understanding Machine Learning Concepts.
Summary of WEEK 1
Regression and beyond.
Handling Data with Different Frequencies.
Analyzing Data Using Fama-Macbeth Regression.
Predictive Models.
Making a Model that Performs Well in Real Life.
Logistic Regression - Solving Classification Problems.
Summary of WEEK 2
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