Applications of Quantitative Finance – Machine Learning

Take a look at what our course modules include in a little more detail:

Module One: Basic Machine Learning
What is machine learning?; Difference between ML, Data Science, and AI; Basic applications in the real world

Module Two: Project Identification
Formulating a machine learning question; Understanding and identifying metrics; Defining success

Module Three: Machine Learning Project Management
DevOps for machine learning projects; Project lifecycle examples, Machine learning team structure

Module Four: Basic Algorithms
Linear regression; Cluster analysis; Supervised vs. unsupervised learning

Module Five: Statistics and Probability
Calculating probability; Chained probability and statistics; Sampling and bias

Module Six: Exploratory Data Analysis (EDA)
Quick view of datasets; Pulling basic insights from data; Visualisation techniques

Module Seven: Linear Algebra
Large data manipulation; Vectors and matrices; Numpy and other tools

Module Eight: Training vs. Testing Data
Splitting data; Avoid ‘small positive’ errors; Pseudo-random division

Module Nine: Overfitting/Common Issues
What is overfitting?; How can you test a model’s accuracy?; Testing a data model?

Module Ten: Recommendation Engines
Elements of recommendation; Hyper-parameter tuning; Trivial vs. non-trivial parameters

Module Eleven: Full Project Example
Walkthrough of full machine learning project