10 AI/ML Related Topics Asked At Google Interviews
Removing missing values from a dataset can cause bias. Various methods like replacing mean, median, and mode can be used to mitigate bias caused by missing data.
Designing a recommendation engine for jobs involves creating a system that learns from a user's behavioral history and interests to generate suitable job recommendations.
Rectified Linear Unit (ReLU) is a widely used activation function in machine learning that allows positive values to pass through and blocks negative values, resulting in faster processing.
Bagging and boosting are ensemble methods used in machine learning. Bagging generates additional training data to decrease variance, while boosting adjusts the weight of observations based on the last classification.
AdaGrad is an adaptive stochastic gradient descent algorithm used for gradient-based optimization in machine learning. It eliminates the need for manual learning rate tuning and provides faster and more reliable convergence.
The degree of freedom for Lasso, a model building technique, refers to the number of estimated predictors. It plays a crucial role in model assessment and selection.
Anomaly detection methods are used to identify unusual patterns or outliers that deviate from expected behavior. These methods include statistical, density-based, and clustering-based approaches.
AUC (Area under the ROC Curve) is an important evaluation metric for classification models. It measures performance across all possible classification thresholds and provides an aggregate measure of performance.
Caching is a high-speed data storage layer that stores a subset of data, allowing faster access for future requests. It enhances performance by reducing the need to search the primary storage location.
Feature selection is a method of reducing data dimensions in predictive analysis. It involves selecting and excluding attributes in a dataset without altering their values, playing a crucial role in machine learning tasks.
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