What you'll learn Master the foundations of supervised Machine Learning & classification modeling in Python Perform exploratory data analysis on model features and targets Apply feature engineering techniques and split the data into training, test and validation sets Build and interpret k-nearest neighbors and logistic regression models using scikit-learn Evaluate model performance using tools like confusion matrices and metrics like accuracy, precision, recall, and F1 Learn techniques for modeling imbalanced data, including threshold tuning, sampling methods, and adjusting class weights Build, tune, and evaluate decision tree models for classification, including advanced ensemble models like random forests and gradient boosted machines Data_Science_in_Python_Classification_Modeling.part1.rar - 995.0 MB Data_Science_in_Python_Classification_Modeling.part2.rar - 995.0 MB Data_Science_in_Python_Classification_Modeling.part3.rar - 995.0 MB Data_Science_in_Python_Classification_Modeling.part4.rar - 592.3 MB
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