[Machine Learning] Model Generalization: Train-Test Splits, Cross Validation

AI is becoming popular, and machine learning is eating up everything. Machine learning allows computers/software/algorithms to learn, recognize, and predict from data.
In this video, we will learn about Model Generalization, the process of identifying errors in a machine-learning model to improve its predictions of new unseen data well. Two examples discussed in this video are Train-Test Splits and Cross Validation, which are both used to measure errors in the model. Through these Model Generalizations, the model can predict more accurately.
The presentation slides and source codes I use here were obtained from the INTEL Machine Learning course. I reorganized the contents for ease of understanding. You can download the original content from the website using the link.
[INTEL Introduction to Machine Learning]
www.intel.com/...
#machinelearning #cross validation #model generalization