⬇️ Get the files and follow along: https://bit.ly/3XErJKS Skills with hyperparameter tuning are a must-have for the DIY data scientist. Think of a machine learning model like a high-performance sports car. Just like a Ferrari needs to be tuned for maximum performance, your models have to be tuned with the dataset at hand. This crash course will teach you the fundamentals of hyperparameter tuning using the scikit-learn library in Python to optimize your machine learning models. ☕ If you found this content useful and would like to support the channel, you can buy me a coffee: https://www.buymeacoffee.com/DaveOnData -------------------------------------------------------------------------------------------- Blog links -------------------------------------------------------------------------------------------- https://machinelearningmastery.com/much-training-data-required-machine-learning/ https://machinelearningmastery.com/k-fold-cross-validation/ https://machinelearningmastery.com/repeated-k-fold-cross-validation-with-python/ -------------------------------------------------------------------------------------------- Save 20% off my machine learning online courses -------------------------------------------------------------------------------------------- Use coupon YOUTUBE. Learn decision trees and the mighty random forest: https://bit.ly/TDWIIntroToML Cluster Analysis with Python online course: https://bit.ly/ClusterAnalysisWithPythonCourse -------------------------------------------------------------------------------------------- Video Chapters -------------------------------------------------------------------------------------------- 00:00 Intro 02:02 Python Isn’t the Most Important 02:49 Supervised Learning 06:07 Splitting Your Data 09:31 Classification vs. Regression 12:51 The Data 14:47 Under/Overfitting 17:12 Controlling Complexity 24:37 Model Tuning Concepts 35:10 Model Tuning with Python 49:06 Model Testing with Python 51:43 Continue Your Learning #machinelearning #hyperparameters #hyperparametertuning