Scaling to Massive Data: Spark Fundamentals

Related lectures (31)

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Covers linear models for classification, focusing on logistic regression and support vector machines.

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.