Dive into Learning Explore the rich courses offerings of MammothClub.

Featuring 50 questions and unlimited attempts! Test your knowledge on using ML foundations to select the appropriate algorithm for a given model scenario, identifying methods to mitigate data imbalance in training data, comparing estimators and transformers, developing a training pipeline, and using Hyperopt's fmin operation to tune a model's hyperparameters.

Featuring 50 questions and unlimited attempts! Test your knowledge on performing random search, grid search, or Bayesian search for tuning hyperparameters, parallelizing single-node models for hyperparameter tuning, understanding the benefits and downsides of using cross-validation over a train-validation split, performing cross-validation as part of model fitting, and identifying the number of models being trained through grid search and cross-validation.

Featuring 50 questions and unlimited attempts! Test your knowledge on using common classification metrics like F1, Log Loss, and ROC AUC, using common regression metrics like RMSE, MAE, and R-squared, choosing the most appropriate metric for a given scenario objective, exponentiating log-transformed variables before calculating evaluation metrics or interpreting predictions, and understanding the impact of model complexity and the bias-variance tradeoff on performance.

Featuring 48 questions and unlimited attempts! Test your knowledge on the differences and advantages of model serving approaches including batch, realtime, and streaming; how to deploy a custom model to a model endpoint; using pandas to perform batch inference; identifying how streaming inference is performed with Delta Live Tables; deploying and querying a model for realtime inference; and splitting data between endpoints for realtime inference.

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