ML Learning Roadmap — Hands-On with scikit-learn

A 30-tutorial, case-study-driven curriculum. Every lesson is one self-contained page with runnable Python, a real dataset, and exercises.

How to use

python -m venv .venv
source .venv/bin/activate
pip install scikit-learn numpy pandas matplotlib

Open a tutorial, copy the code blocks into a .py file or Jupyter notebook, and run them.
Complete the Exercises at the end of each tutorial before moving on.

Total: ~7–8 weeks at 1 tutorial per weekday.

#	Tutorial	Level
01	Iris Flower Classification	Beginner
02	Wine Quality EDA	Beginner
03	Train/Test Baseline	Beginner
04	Cross Validation	Beginner
05	Feature Scaling	Beginner
06	Categorical Encoding	Beginner
07	Missing Value Imputation	Beginner
08	scikit-learn Pipelines	Intermediate
09	Column Transformer	Intermediate
10	Polynomial Features	Intermediate
11	Linear Regression — Housing	Intermediate
12	Ridge & Lasso Regression	Intermediate
13	Logistic Regression — Breast Cancer	Intermediate
14	KNN Classification	Intermediate
15	Decision Trees	Intermediate
16	Random Forest	Intermediate
17	Gradient Boosting	Advanced
18	SVM Classification	Advanced
19	Naive Bayes Text	Advanced
20	Classification Metrics	Intermediate
21	ROC / AUC Curves	Intermediate
22	Learning Curves	Advanced
23	Grid Search CV	Advanced
24	Randomized Search	Advanced
25	Feature Selection	Advanced
26	PCA Dimensionality Reduction	Intermediate
27	K-Means Clustering	Intermediate
28	Hierarchical Clustering	Intermediate
29	Ensemble Voting & Stacking	Advanced
30	Capstone — Titanic Survival	Advanced