A full university-level machine learning course – for free. New lectures every week.

Designed as a first course for engineers, program managers, and data professionals who want to learn: the details of important machine learning algorithms; professional model building; and design patterns for building practical machine learning systems.

This lecture covers:
Step one of evaluating models (there will be several more in the course). Why to evaluate models. Training set, validation set, and test set. Hyperparameters and the model evaluation pattern. The concept of generalization. Confusion matrices, true positives, false positives, false negatives and true negatives. Evaluation metrics: accuracy, precision, recall, false positive rate and true positive rate.

You can get slides for this lecture from: https://livingmachinelearning.com/courses/machinelearning/slides/02%20–%20Basics%20of%20Evaluating%20Models.pptx

And you can view the entire course in this playlist: https://www.youtube.com/playlist?list=PLrQmbzbRJ5mwDinvDEJ5B-KDZlPM-sCYO

Learn more at: www.livingmachinelearning.com/

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