FeaturedUdemy
Model Validation in Python
4.9(896)30k studentsINTERMEDIATEEnglish
Korshub's take
Evaluating Machine Learning Model Performance
Building machine learning models is straightforward, but ensuring they perform accurately on unseen data requires rigorous evaluation. This intermediate course teaches practical model validation techniques in Python to help you objectively assess model quality and reliability.
Who Is This Course For?
Ideal for data analysts and Python developers who want to validate classification and regression algorithms effectively before deploying them.
Key Takeaways
- Validation Fundamentals: Master essential strategies to evaluate how well models generalize to new data.
- Classification Validation: Test classification models using a dataset of tic-tac-toe endgame scenarios.
- Regression Validation: Evaluate regression algorithms using FiveThirtyEight’s Halloween candy power ranking data.
About this course
Machine learning models are easier to implement now more than ever before. Without proper validation, the results of running new data through a model might not be as accurate as expected. Model validation allows analysts to confidently answer the question, how good is your model? We will answer this question for classification models using the complete set of tic-tac-toe endgame scenarios, and for regression models using fivethirtyeight’s ultimate Halloween candy power ranking dataset. In this course, we will cover the basics of model validation, discuss various validation techniques, and begin to develop tools for creating validated and high performing models.
Price
Free
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