APPLIED MACHINE LEARNING

Catalog description

Explores machine learning basics (variance and bias, underfitting and overfitting, etc). Reviews linear algebra and Numpy. Examines k-nearest neighbors, linear classification (perceptron and online learning), and linear and non-linear regression. Explores applications in housing price prediction (Kaggle contest) and text classification (sentiment analysis).