See also the notebooks using Titanic survival to teach classification with machine learning. These cover the essentials of machine learning classification, and include logistic regression. Random Forest, PyTorch and TensorFlow models. See here: https://pythonhealthcare.org/titanic-survival/
Splitting data into training and test sets
Using logistic regression to diagnose breast cancer
Adding standard diagnostic performance metrics to a ml diagnosis model
How do you know if you have gathered enough data? By using learning rates.
Working with ordinal and categorical data
Choosing between models with stratified k-fold validation
Optimising scikit-learn machine learning models with grid search or randomized search
Visualising accuracy and error in a classification model with a confusion matrix
Reducing data complexity, and eliminating covariance, with principal component analysis
Feature selection 1 (univariate statistical selection)
Feature selection 2 (model selection; forward selection)
Feature selection 3 (model selection; backwards selection)
Grouping unlabelled data with k-means clustering
Linear regression with scikit learn
Random Forests regression (suitable for more complex data sets than linear regression)
Worked machine learning example (for HSMA course)
Simple machine learning model to predict emergency department (ED) breaches of the four-hour target
Oversampling to correct for imbalanced data using naive sampling or SMOTE
