Automatic Machine Learning @SirajRaval
Automatic Machine Learning  @SirajRaval
Uploaded July 2019 | Updated September 2026, 2 weeks ago
Automatic Machine Learning or "AutoML" is a field of Artificial Intelligence thats gaining a lot of interest lately. The idea is that doing any kind of task related to machine learning involves a whole lot of steps like cleaning a dataset, choosing a model, deciding what the right configurations of that model should be, deciding what the most relevant features are etc. The goal of AutoML is to automate all of that up to a point where all a data scientist would need to do is tell a machine to perform some task using a dataset and wait for it to learn how by itself. In this episode, i'm going to explain several popular AutoML techniques, then compare top AutoML frameworks like AutoKeras, Auto Sklearn, h20, Ludwig, etc. to help you decide which one will be the best for your needs.

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AutoML Framework #1 - MLBox
github.com/AxeldeRomblay/MLBox

AutoML Framework #2 - Auto Sklean
github.com/automl/auto-sklearn

AutoML Framework #3 - TPOT
github.com/EpistasisLab/tpot

AutoML Framework #4 - H20
docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

AutoML Framework #5 - Autokeras
autokeras.com

AutoML Framework #6 - Ludwig
uber.github.io/ludwig

My video on Ludwig:
youtube.com/watch?v=w45t3itM5NM

The DARTS paper:
arxiv.org/abs/1806.09055

DARTS in PyTorch:
github.com/quark0/darts

Cool write-up on Simulated Annealing:
santhoshhari.github.io/simulated_annealing

Cooler write-up on Bayesian Optimization:
krasserm.github.io/2018/03/21/bayesian-optimization

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Automatic Machine Learning

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