Fundamentals of Dask Transcripts
Chapter: Dask-ML
Lecture: Introducing Dask-ML
Login or
purchase this course
to watch this video and the rest of the course contents.
0:00
So let's jump in. First, I want to tell you that ml.dask .org, which you can check out,
0:08
describes Dask-ML as a single unified interface around the familiar NumPy, Pandas and Scikit-learn APIs. Now this is true.
0:19
It implements the scikit-learn API and inter operates between Dask Data Frame and
0:24
Dask Array to provide a seamless experience for machine learning tasks in a distributed setting.
0:30
Wowie. So, let me just tell you what we're going to look at. In this chapter. I'm very excited to bring to you 1st.
0:38
I'll start by demonstrating scikit-learn which you may be aware is a library for machine
0:43
learning in Python. So this will be a crash course in using scikit-learn for
0:46
machine learning. Then we'll jump into solving compute bound challenges withJoblib and Dask After this will solve memory bound problems.
0:57
Using Dask-ML estimators, will end up with some references where you can learn more. Now it's time to jump into the notebook.