Machine Learning at ODSC 2023

1

Materials for the Causal AI: from Data to Action Workshop
Causal AI: from Data to Action
Materials for the Missing Data: A Synthetic Data Approach for Missing Data Imputation Workshop
Missing Data: A Synthetic Data Approach for Missing Data Imputation
Materials for the Full-Stack Machine Learning for Data Scientists Workshop
Full-Stack Machine Learning for Data Scientists
Big Data Analysis with PySpark
Materials for the Space Science with Python - Enabling Citizen Scientists Workshop
Space Science with Python - Enabling Citizen Scientists
Materials for the Automate Machine Learning Workflows with PyCaret 3.0New Workshop
Automate Machine Learning Workflows with PyCaret 3.0
Materials for the Feature Engineering With Signal Types Workshop
Feature Engineering With Signal Types

2

Recommender Systems Methods and Usage of Graphs for Recommendations
Material for the Build Classification and Regression Models with Spark on AWS Tutorial
Build Classification and Regression Models with Spark on AWS

3

Evaluating Recommendation Algorithms at Delivery Hero
Adversarial Validation and Training in Stock Market Price Prediction
PyTorch 2.1 - New Developments
Representation Learning on Graphs and Networks
Causality and LLMs
Building Robust and Scalable Recommendation Engines for Online Food Delivery
Human-Friendly, Production-Ready Data Science with Metaflow
State of AI in Human Resource Functions: Unique Opportunities and Challenges
Botnets detection at scale - Lesson learned from clustering billions of web attacks into botnets
Time Series Forecasting for Managers - All forecasts are wrong but some are useful

Course curriculum