Learn to prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.
Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.
Learn how to scrape data from financial documents using Regular Expressions and BeautifulSoup
Learn how to apply to NLP to financial statements
NLP Analysis on 10-k financial statements to generate an alpha factor.
In this lesson, Luis will teach you the foundations of deep learning and neural networks. You'll also implement gradient descent and backpropagation in python, right here in the classroom!
Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
Learn how to use PyTorch for building deep learning models
Learn how to use recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time.
In this lesson, you'll learn about embeddings in neural networks by implementing the Word2Vec model.
Implement a sentiment prediction RNN for predicting whether a movie review is positive or negative!
Build a deep learning model to classify the sentiment of messages.
Learn about machine learning from a bird's-eye-view.
Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
Learn about metrics to evaluate models and about how to avoid over- and underfitting.
Learn about random forest models and how to use them to combine alpha factors.
Learn to engineer features such as market dispersion, market volatility, sector and date parts. Also learn to engineer targets (labels) that are robust to market changes over time.
Learn about an issue with non-independent labels that comes up during alpha combination with machine learning models.
Feature importance helps us decide how relevant each feature is to a machine learning model's predictions. Learn about two methods for calculating feature importance.
Build a random forest to generate better alpha.
Backtesting helps you determine whether or not your strategies can be generalizable to future unseen data.
Learn about how to make the portfolio optimization in a backtest more realistic, and also more computationally efficient.
Use performance attribution to determine how each factor contributed to the portfolio's results.
Build a backtester using Barra data.