The mid-level scikit-learn exam. Built by the people who maintain it.

• Advanced ML knowledge — Proficiency in a broad range of machine learning algorithms and the ability to select appropriate models for specific problems.

• Programming expertise — Strong coding skills in Python, with experience in optimizing code for performance and scalability.

• Data handling and engineering — Ability to handle large datasets, including data extraction, transformation, and loading processes.

• Feature engineering — Experience in creating and selecting features to improve model performance.

• Tuning and optimization — Proficiency in hyperparameter tuning, model selection, and ensemble methods to improve model performance.

• Critical thinking — Approach complex problems systematically and evaluate multiple solutions, including diagnosing issues in a model pipeline.

• Business expertise — How ML projects align with business goals and how to translate technical results into actionable business insights.

Machine learning concepts

The advanced mental model. Probabilistic outputs, regularization regimes, and what overfitting does to soft predictions.

• Supervised and unsupervised, regression, classification, clustering, dimensional reduction
• Model families: tree-based, linear, ensemble, neighbors
• Regularization: L1, L2, Elasticnet
• Hard and soft predictions: predict vs predict_proba
• Overfitting and underfitting, impact on soft predictions

Model building and evaluation

Pick the baseline, regularize the noise, ensemble when warranted, and choose the metric that fits the problem.

• Linear models as baselines
• Handling correlation with regularization and feature selection
• Bagging and boosting, the working ensemble methods
• Choosing metrics for outliers and imbalanced settings

Interpretation and communication

Read the plot, name the failure mode, explain it without using the word probability twice.

• Visualizing results with intermediate matplotlib and seaborn techniques
• Interpreting model outputs and performance metrics
• Communicating results to non-technical stakeholders

Data preprocessing

Heatmaps, PCA, polynomial features, label propagation. The shaping work that makes a real-world dataset trainable.

• Loading parquet datasets
• Heatmaps and PCA for first look
• Identifying strongly correlated features
• Missing values in the target via label propagation
• Feature engineering with PolynomialFeatures, SplineTransformer
• Combining features with FeatureUnion

Model selection and validation

Group structure, non i.i.d. data, nested CV, stable hyperparameters across folds.

• Cross-validation with group structure and non i.i.d. data
• Hyperparameter tuning: GridSearchCV, RandomSearchCV
• Stability of optimal hyperparameters via nested cross-validation

Associate Practitioner

Junior data scientist. Fundamental ML, preprocessing, evaluation.

Professional

Mid-level. Regularization, ensembles, feature engineering, nested CV.

Expert

Senior practitioner. Production ML, scaling, governance.

Do I need Associate before Professional? No. Associate is recommended as a stepping stone but not required. If you have a year or two of working data science with scikit-learn, you can sit Professional directly.

Is there a hands-on component? Yes. The Professional exam adds one hands-on lab on top of the multiple-choice questions. You will write and tune a small pipeline against a held-out dataset, in a sandboxed scikit-learn environment.

What about retakes? One retake is included with your registration. After that, retakes are discounted. There is a 21-day cool-down between attempts so you can revisit weak topics on Skolar.

Is the credential verifiable? Yes. Every passing candidate gets a credential ID and a public verification page on probabl.ai. Recruiters can confirm validity without contacting you.

Does it expire? The Professional certification is valid for 3 years. Renew by passing the Expert exam, or by re-taking Professional at a discount.