Master cross-validation, preprocessing, pipelines, advanced metrics, and hyperparameter tuning.
1.Cross-Validation
Learn k-fold cross-validation for more robust model evaluation than a single train/test split.
2.StandardScaler Deep Dive
Master StandardScaler: understand how it works and when to use it.
3.OneHotEncoder for Categories
Learn OneHotEncoder to convert categorical features into numerical format for ML models.
4.Building Pipelines
Learn to create scikit-learn Pipelines that chain preprocessing and modeling steps.
5.ColumnTransformer for Mixed Data
Use ColumnTransformer to apply different preprocessing to different columns.
6.Precision, Recall, and F1
Learn precision, recall, and F1-score for better evaluation of classification models.
7.ROC Curves and AUC
Learn ROC curves and AUC for threshold-independent model evaluation.
8.Ridge & Lasso Regularization
Learn regularization techniques to prevent overfitting in linear models.
9.GridSearchCV Basics
Learn to systematically search for optimal hyperparameters using GridSearchCV.
10.RandomizedSearchCV
Learn RandomizedSearchCV for efficient hyperparameter search with many parameters.
11.Random Forest Introduction
Learn Random Forest - a powerful ensemble method combining many decision trees.
12.Feature Importance Analysis
Learn to extract and interpret feature importances from tree-based models.