| Summary: | "Inside the course, you'll learn how to: set up a Python development environment correctly; gain complete machine learning toolsets to tackle most real-world problems; understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them; combine multiple models with by bagging, boosting or stacking; make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data; develop in Jupyter (IPython) notebook, Spyder and various IDE; communicate visually and effectively with Matplotlib and Seaborn; engineer new features to improve algorithm predictions; make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data; use SVM for handwriting recognition, and classification problems in general; use decision trees to predict staff attrition; apply the association rule to retail shopping datasets."--Resource description page
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