LIVE TRAINING: October 28th: Gradient Boosting for Prediction and Inference

Name: LIVE TRAINING: October 28th: Gradient Boosting for Prediction and Inference
Start: 2021-10-28T19:00:00+02:00
End: 2021-10-28T23:00:00+02:00

Hosted By

Iryna P.

LIVE TRAINING: October 28th: Gradient Boosting for Prediction and Inference

Details

This is a PAID event.

Registration is required: https://aiplus.training/live/gradient-boosting-for-prediction-and-inference-live-training/

Level INTERMEDIATE

Instructor's bio: Brian Lucena,PhD, Principal | Numeristical

Brian Lucena is Principal at Numeristical and the creator of StructureBoost, ML-Insights, and SplineCalib. His mission is to enhance the understanding and application of modern machine learning and statistical techniques. He does this through academic research, open-source software development, and educational content such as live stream classes and interactive Jupyter notebooks. Additionally, he consults for organizations of all sizes from small startups to large public enterprises. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

Abstract:

Gradient Boosting is widely used in prediction problems across industry and academia. Common applications include fraud detection, home price prediction, and loan default prediction, just to name a few. This course is an intensive hands-on workshop with real data sets focused on using Gradient Boosting for classification and regression problems. Participants will gain valuable experience training, evaluating, and drawing conclusions from Gradient Boosting models. They will gain familiarity with Gradient Boosting packages such as XGBoost, LightGBM, CatBoost, and StructureBoost. By the end of the course, participants will feel confident that they understand the details and parameters behind Gradient Boosting, and be able to present, criticize, and defend the models they create.

Course Outline

Background: Decision Trees, and Random Forests
Gradient Boosting: Definition and History
Review of gradient Boosting Packages
Interpreting and Understanding Gradient Boosting Models
Application to Medical Data

Which knowledge and skills you should have?
This course is geared to data scientists of all levels who wish to gain a deep understanding of Gradient Boosting and how to apply it to real-world situations. The ideal participant will have some experience with building models. They should know the Python data science toolkit (numpy, pandas, scikit-learn, matplotlib) and have experience fitting models on training sets, making predictions on test sets, and evaluating the quality of the model with metrics.

What is included in your ticket?