The Machine Learning Process
This chapter starts Part 2 of this book, where we'll illustrate how you can use a range of supervised and unsupervised machine learning (ML) models for trading. We will explain each model's assumptions and use cases before we demonstrate relevant applications using various Python libraries. The categories of models that we will cover in Parts 2-4 include:
- Linear models for the regression and classification of cross-section, time series, and panel data
- Generalized additive models, including nonlinear tree-based models, such as decision trees
- Ensemble models, including random forest and gradient-boosting machines
- Unsupervised linear and nonlinear methods for dimensionality reduction and clustering
- Neural network models, including recurrent and convolutional architectures
- Reinforcement learning models
We will apply these models to the market, fundamental, and alternative data sources...
