Statistics for Machine Learning for Investment Professionals
Examine modeling frameworks and get familiar with Python and R languages to improve investment decision making.
About This Course
This course introduces learners to foundational statistical concepts underpinning machine learning as well as advanced AI techniques used in the investment profession. Demonstrating core modeling frameworks along with carefully selected real-world investment practice examples, the course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary). The motivation is to demonstrate the elegance — and speed — simple programming brings to the investment decision-making process.
This course is part of a data science series. It is suggested, but not required, that learners complete the courses in the recommended order below to ensure uniform foundational knowledge.
- Data and Statistics Foundation for Investment Professionals
- Statistics for Machine Learning for Investment Professionals
- Machine Learning for Investment Professionals
- Natural Language Processing for Investment Professionals
What You’ll Learn
- Describe the importance of identifying information patterns for building models
- Explain probability concepts for solving investing problems
- Explain the use of linear regression and interpret related Python and R code
- Describe gradient descent, explain logistic regression, and interpret Python and R code
- Describe the characteristics and uses of time-series models
Learn to explain use of linear regression, interpret Python and R code and describe gradient descent, logistic regression and the uses of time-series models.
Learn data techniques used in machine learning and ways to tell the data story using visualizations in report writing.
Gain an understanding of machine learning along with the technical and soft skills needed to use it in the investment process.
Learn to leverage NLP in sentiment analysis for use in investment valuation models and the decision-making process.