Dive into Learning Explore the rich courses offerings of MammothClub.

Featuring 48 questions and unlimited attempts! Test your knowledge on how to compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries, remove outliers from a Spark DataFrame based on standard deviation or IQR, create visualizations for categorical or continuous features, and compare two categorical or two continuous features using the appropriate method.

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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