An end-to-end introduction to machine learning: data preparation, feature engineering, regression, classification, clustering and model evaluation.
Topics covered
NumPy and pandas; regression metrics; logistic regression, KNN, SVM, Naive Bayes and random forests; K-Means; Apriori and Eclat; UCB and Thompson Sampling; NLP; CNN basics; PCA; model selection, cross-validation and XGBoost.
