Ementorhub

What you'll learn

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets

Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes

Utilize powerful Python libraries to implement machine learning algorithms in case studies

Learn about factor models and regime switching models and their use in investment management

Skills you'll gain

Investment Management

Machine Learning

Financial Modeling

Unsupervised Learning

Portfolio Management

Statistical Methods

Supervised Learning

Regression Analysis

Applied Machine Learning

Risk Management

Asset Management

Feature Engineering

Dimensionality Reduction

Predictive Modeling

There are 5 modules in this course

The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis. You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept. At the end of this course, you will master the various machine learning techniques in investment management.

Machine learning techniques for robust estimation of factor models

Machine learning techniques for efficient portfolio diversification

Machine learning techniques for regime analysis

Identifying recessions, crash regimes and feature selection