⬇️ 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
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Blog links
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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/
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Learn decision trees and the mighty random forest:
https://bit.ly/TDWIIntroToML
Cluster Analysis with Python online course:
https://bit.ly/ClusterAnalysisWithPythonCourse
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Video Chapters
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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