Automatic Machine Learning

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. It would mean so much to me if you subscribe to my YouTube Channel! https://bit.ly/2SvhG7v Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Website: www.sirajraval.com AutoML Framework #1 - MLBox https://github.com/AxeldeRomblay/MLBox AutoML Framework #2 - Auto Sklean https://github.com/automl/auto-sklearn AutoML Framework #3 - TPOT https://github.com/EpistasisLab/tpot AutoML Framework #4 - H20 http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html AutoML Framework #5 - Autokeras https://autokeras.com/ AutoML Framework #6 - Ludwig https://uber.github.io/ludwig/ My video on Ludwig: https://www.youtube.com/watch?v=w45t3itM5NM The DARTS paper: https://arxiv.org/abs/1806.09055 DARTS in PyTorch: https://github.com/quark0/darts Cool write-up on Simulated Annealing: https://santhoshhari.github.io/simulated_annealing/ Cooler write-up on Bayesian Optimization: http://krasserm.github.io/2018/03/21/bayesian-optimization/ ---------------------------------------------------------------------------------------------------- Make Money with Tensorflow 2.0: https: