Corporate AI & Technology Training Catalog

Machine Learning & Applied Data Science Machine Learning Fundamentals RuneLab Academy · 12 hours

Supervised and unsupervised learning, train/test splits, overfitting, core metrics and first models in scikit-learn, so technical teams can frame a business problem as an ML task and build a sound baseline.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Advanced Machine Learning RuneLab Academy · 12 hours

Gradient boosting, ensembles and stacking, hyperparameter optimisation with Optuna, imbalanced data, probability calibration and custom loss functions for experienced data scientists building production-grade models.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Machine Learning for Data Analysis RuneLab Academy · 12 hours

Takes analysts beyond classic reporting with segmentation, driver analysis, anomaly flagging and simple predictive models in Python, scikit-learn and SHAP, producing analyses that tie insight to business decisions.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Feature Engineering for Machine Learning RuneLab Academy · 18 hours

Categorical encoding, time and window-based features, target encoding, interaction and text features, feature selection and feature stores: a systematic approach to the step that most improves model performance on tabular data.

18 hours

Live Course

5 modules

Machine Learning & Applied Data Science Model Evaluation and Explainability RuneLab Academy · 12 hours

Choosing the right metrics, evaluation tied to business KPIs, error analysis, fairness checks and model explanation with SHAP, LIME and PDP, so models can be presented to stakeholders reliably and understandably.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Model Validation and Model Risk Management RuneLab Academy · 12 hours

Independent model validation: data and conceptual soundness checks, backtesting, stability (PSI), challenger models, stress tests and findings reporting, producing a validation report aligned with a model risk management framework.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Time Series Forecasting RuneLab Academy · 12 hours

Time series forecasting with ARIMA, ETS, Prophet and LightGBM-based approaches, working hands-on with seasonality, exogenous variables, hierarchical series, backtesting and prediction intervals to build reliable forecasts.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Anomaly Detection with Machine Learning RuneLab Academy · 12 hours

Detecting unusual behaviour in unlabelled data with statistical methods, Isolation Forest, LOF, autoencoders and time series anomaly techniques, including threshold setting, alert fatigue and evaluation strategies.

12 hours

Live Course

5 modules

End-to-end machine learning with Scikit-learn, XGBoost, LightGBM, and CatBoost, covering feature engineering, Optuna hyperparameter tuning, imbalanced data, and SHAP/LIME explainability.

24 hours

Live Course

3 modules

Machine Learning & Applied Data Science Fraud Detection with Machine Learning RuneLab Academy · 12 hours

Behavioural features, gradient boosting, anomaly scores and hybrid rule-model setups for payment, insurance and account fraud, with hands-on work on extreme class imbalance, delayed labels and operational threshold management.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Risk Scoring and Credit Scorecard Modelling RuneLab Academy · 12 hours

Variable analysis with WoE/IV, logistic regression scorecards, gradient boosting alternatives, reject inference and score calibration for credit, insurance and supplier risk, building explainable and auditable risk scores.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Customer Churn and Propensity Modelling RuneLab Academy · 12 hours

Churn, purchase and cross-sell propensity models covering target definition, customer behaviour features, survival analysis, uplift modelling and campaign targeting, turning predictions into measurable retention and sales actions.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Recommendation Systems RuneLab Academy · 12 hours

Collaborative filtering, content-based and hybrid recommenders, matrix factorisation, implicit feedback, cold-start strategies and ranking metrics for building personalised product and content recommendation engines.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Predictive Maintenance with Machine Learning RuneLab Academy · 12 hours

Failure probability and remaining useful life (RUL) prediction from sensor, telemetry and maintenance logs, with hands-on signal feature engineering, health indices, early-warning thresholds and maintenance cost optimisation.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Demand Forecasting for Supply Chain and Retail RuneLab Academy · 12 hours

SKU-location demand forecasting covering promotions, price elasticity, stockout censoring adjustment, intermittent demand (Croston/ADIDA), hierarchical reconciliation and probabilistic forecasts tied to safety stock.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Optimization and Decision Systems RuneLab Academy · 12 hours

Bridging predictive models and mathematical optimisation: LP/MILP formulation, capacity, scheduling, routing and budget/price allocation with OR-Tools and Pyomo, plus stochastic and contextual decision systems under uncertainty.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Model Development with XGBoost and LightGBM RuneLab Academy · 12 hours

Mastering tabular gradient boosting with XGBoost, LightGBM and CatBoost: tree-growth strategies, native categorical and missing-value handling, ranking/quantile/custom objectives, monotonic constraints, Optuna and fast inference.

12 hours

Live Course

5 modules

Machine Learning & Applied Data Science Automated Machine Learning (AutoML) RuneLab Academy · 12 hours

Automated model selection, hyperparameter search and multi-layer stacking with AutoGluon, FLAML and Optuna, covering compute budget management, leakage checks and moving from rapid baselines to production.

12 hours

Live Course

5 modules