Hyperparameter Tuning: How to Optimize Your Machine Learning Models!

[@DaveOnData]

Save 20% off my machine learning online courses using code KZbin ⬇ Cluster Analysis with Python online course: bit.ly/ClusterAnalysisWithPythonCourse My "Intro to Machine Learning" online course: bit.ly/TDWIIntroToML

[@ShyamKakkad-cs4dl]

by far the best teacher on youtube.

[@DaveOnData]

Whoa! That's a huge compliment. Thank you. These words are much appreciated.

[@ShyamKakkad-cs4dl]

@@DaveOnData no, thank you for all the good content. it's hard to find good teachers through all of the saturation

[@michaelt312]

When you have a meeting starting any moment and David drops a new video... Will be playing on the Bluetooth headset now and watching it later. #ForTheAlgorithm

[@DaveOnData]

Woohoo! The support, as always, is greatly appreciated. The algo does love watch time. 🤣

[@gustavoq5813]

Thanks for this, Dave! Very useful information.

[@DaveOnData]

You are welcome! So glad you enjoyed the crash course.

[@mekersemito]

Hello David, thanks for the helpful video! I watched the whole lecture and it gave me good confidence on my code as well. I want to ask you I actually work with gauussian process regression with the RBF kernel. And my reference book is by C. E. Rasmussen & C. K. I. Williams which is a very nice book, but since I'm starting to use python I have some difficulity in understanding their algorithm in estimating the hyperparameters. So my question to you is how do I write the code if I have a noisy observed data like 200 and want to predict at 50 points how do i write the code to estimate my three hyperparameters. should i write the code in algorithm or in python code, i have everything i just got stuck there your help is a lot to me, thank you very much!