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