Hi, some suggestions based on course developed by us (removed some stuff to fit in 20 hours):

• Concept of learning: supervised, unsupervised, and semi-supervised.
• Regression: simple and multiple linear regression, polynomial regression, regularization, SVR, decision trees, random forest regression.
• Classification: Logistic regression, K-NN, SVM, kernel SVM, naive Bayes, decision trees, random forests.
• Dimensionality Reduction: PCA, UMAP.
• Clustering: k-means, hierarchical clustering.
• Feature selection and extraction.
• Evaluation and generalization: training set, test and validation, cross-validation, parameter tuning, grid search, XGBoost.
• Python notebooks and libraries for ML: numpy, pandas, matplotlib, seaborn, scikit-learn, scipy and others.

Hope it’s useful!

Sometimes, but I try avoid those if I see them coming. The worst thing is a meeting’s flow-wrecking potential, even if the meeting itself is relevant.

u/bengaliguy beat me to it! Meetings!