Train in Data Courses

Clustering and Dimensionality Reduction

Master clustering and dimensionality reduction techniques in ML. Explore K-means, PCA, UMAP, DBSCAN, and more through hands-on Jupyter notebooks, real-world case studies, and rigorous theory.

• Train in Data
• Self-Paced
• Paid Course

Forecasting with Machine Learning

Master time series forecasting using ML models like linear regression, random forests, and XGBoost. Learn backtesting strategies, multistep forecasting, and error metrics for single and multiple series.

• Train in Data
• Self-Paced
• Paid Course

Feature Engineering for Time Series Forecasting

Master feature engineering for time series forecasting—create lag, window, and seasonal features, handle missing data, remove outliers, and build regression-ready tabular datasets.

• Train in Data
• Self-Paced
• Paid Course

Feature Engineering for Machine Learning

Master feature engineering for ML with techniques covering missing data imputation, categorical encoding, variable transformation, discretization, and feature creation using pandas, Scikit-learn, and Feature-engine.

• Train in Data
• Self-Paced
• Paid Course

Feature Selection for Machine Learning

Master feature selection techniques for ML, including filter, wrapper, and embedded methods, to build simpler, faster, and more reliable models using Python and Feature-engine.

• Train in Data
• Self-Paced
• Paid Course

Machine Learning Interpretability

Master machine learning interpretability using LIME, SHAP, PDP, ALE plots, and more. Explain black-box models, build trust, and implement techniques in Python with hands-on demos.

• Train in Data
• Self-Paced
• Paid Course

Hyperparameter Optimization for Machine Learning

Master hyperparameter optimization techniques including grid search, Bayesian optimization, and multi-fidelity methods to boost ML model performance using Python libraries like Scikit-Optimize, Hyperopt, and Optuna.

• Train in Data
• Self-Paced
• Paid Course

Machine Learning with Imbalanced Data

Master techniques for handling imbalanced datasets, including SMOTE, resampling, cost-sensitive learning, and ensemble methods to improve classifier performance and reduce minority class misclassification.

• Train in Data
• Self-Paced
• Paid Course