The Dask-ML Workshop Enables your Team to Machine Learning at Scale

DASK-ML WORKSHOP

Dask-ML Workshop Overview

This half-day workshop introduces participants to Dask-ML for scaling standard Python machine learning tools (e.g., Scikit-Learn, XGBoost). Participants apply various pre-built models on moderate-to-large datasets to learn best practices for parallel & out-of-core machine learning.

Keep reading below for more information about this training course or click the button here to fill out a workshop request

Prerequisites

We assume participants have prior experience using the Python language and, in particular, using standard Python tools for data analysis (notably NumPy, Pandas, Jupyter). Participants should also have some prior exposure to Scikit-Learn for machine learning and to Dask for scaling data analysis in Python.

Learning Objectives

At the end of this workshop, participants should be able to:

 

  • Deploy incremental learning with partial fit models for large datasets

 

  • Exploit parallelism for cross-validation and hyperparameter grid-search using standard Python idioms

 

  • Implement parallel prediction using Dask-ML meta-estimators

 

  • Scale out linear models (e.g., Linear/Logistic Regressors) & XGBoost with Dask-ML

To be contacted by a Quansight representative, or for more information, please fill out the form at the link below.

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