Develop an AI system to solve a real-world problem : Training, Testing, and Validation

The video tutorial covers the evaluation of supervised learning models, focusing on accuracy and overfitting. It introduces a three-step pipeline to manage data and prevent overfitting, using training, testing, and validation datasets. An example of email classification is provided to illustrate model training and testing. The importance of having more data for better model performance is emphasized, and the implementation of these concepts using Scikit-learn is demonstrated.

How can we adjust the parameters of a model to reduce overfitting?

What is the significance of the validation data in the training process?

Discuss the importance of having a large amount of data in supervised learning.

What factors can affect the accuracy of a model in predicting outcomes?