Introduction to Machine Learning with SciKit Learn

This intermediate path introduces the core workflow for building machine learning models with scikit-learn. You will learn how to prepare data, train baseline models, make predictions, and evaluate results using appropriate metrics. The path covers both regression and classification, including linear and regularized regression, logistic regression, decision trees, and k-nearest neighbors. You will also explore ensemble methods such as bagging, boosting, and stacking to combine models for stronger performance. As you progress, you will practice model selection techniques including cross-validation, grid search, and random search. The path is designed for learners who have some programming experience and want a structured introduction to applied machine learning in Python.

Syllabus

Preprocess datasets by handling missing values, encoding categories, scaling features, and creating train-test splits
Train regression and classification models using scikit-learn
Evaluate model performance with metrics for regression and classification tasks
Apply regularization techniques such as ridge, lasso, and elastic net regression
Compare classifiers including logistic regression, decision trees, and k-nearest neighbors
Optimize models with cross-validation, grid search, and random search
Build ensemble models using bagging, boosting, and stacking techniques