ML-Vorstellungsgespräche in Python üben

In the first chapter of this course, you'll perform all the preprocessing steps required to create a predictive machine learning model, including what to do with missing values, outliers, and how to normalize your dataset.

In the second chapter of this course, you'll practice different several aspects of supervised machine learning techniques, such as selecting the optimal feature subset, regularization to avoid model overfitting, feature engineering, and ensemble models to address the so-called bias-variance trade-off.

In the third chapter of this course, you'll use unsupervised learning to apply feature extraction and visualization techniques for dimensionality reduction and clustering methods to select not only an appropriate clustering algorithm but optimal cluster number for a dataset.

In the fourth and final chapter of this course, you'll really step it up and apply bootstrapping and cross-validation to evaluate performance for model generalization, resampling techniques to imbalanced classes, detect and remove multicollinearity, and build an ensemble model.