Chapter 1 - The Machine Learning Landscape

The topics below are organized according to the HOML, but the level of understanding I expect is better reflected in the notes covered in class than the textbook (which is much more in depth).

5. Chapter 1 - The Machine Learning Landscape#

  • Supervised vs Un-supervised

  • Regression vs Classification

  • ML tasks, applications

  • Training and testing

  • Parameters and hyper-parameters

6. Chapter 4 - Training Models#

  • Linear regression

  • The modeling process (sequence of steps)

  • Features and target

  • Model parameters

  • Predictions and residuals

  • Loss and Cost function

  • Assessing linear models

  • Correlation and co-linearity

  • Bias and variance

  • Over- and under-fitting

  • Regularization

  • Lasso, Ridge, Elastic Net

  • Polynomial Regression

  • Feature scaling and engineering

Contents