As you can see from the above image, confident learning is about estimating the likelyhood of the data being labeled correctly based upon the confidence of the model. If the model confidence is above the threshold confidence (The Tj parameter, tdog, tfox tcow) and if the confidence of the model prediction is higher than the threshold but the label is different, then we predict a wrong label
This book is about methods and ways to understand AI and data modeling and how to utilize the different ways of interpreting machine learning models. It gives the basis and then dives into the different types of models and methods you can use. It separates the models into specific to models or model families, and model agnostic.
This book is about the practical implementation of machine learning models. It goes through why machine learning should be used, how to implement it, and how to execute in all phasers of the machine learning life-cycle.