Train in Data

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Clustering and Dimensionality Reduction

Unlock the Power of Unsupervised Machine Learning and Data Mining with Cluster Analysis and Dimensionality Reduction

CourseBy Dalibor Veljkovic

Forecasting with Machine Learning

Forecast single and multiple time series with machine learning models. Implement backtesting to evaluate models before deployment.

CourseBy Kishan Manani

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Feature Engineering for Time Series Forecasting

Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.

CourseBy Kishan Manani

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Feature Engineering for Machine Learning

Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.

CourseBy Soledad Galli

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Feature Selection for Machine Learning

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

CourseBy Soledad Galli

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Machine Learning Interpretability

Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.

CourseBy Soledad Galli

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Hyperparameter Optimization for Machine Learning

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.

CourseBy Soledad Galli

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Machine Learning with Imbalanced Data

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

CourseBy Soledad Galli

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14 Common Feature Engineering Questions - FREE

Sole answers 14 of the most common questions students ask about feature engineering for machine learning, covering topics such as encoding, scaling, imputation, and outlier handling. It offers clear, practical answers grounded in real-world experience to help practitioners build better, more robust machine learning pipelines

Digital file1 fileBy Soledad Galli

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7 Takes on Working with Imbalanced Data - FREE

Sole discusses three recent articles that shift the conversation around handling imbalanced datasets. These articles explore whether generating synthetic data is effective with current state-of-the-art algorithms, and examine the implications of using synthetic data on model performance and deployment.

Digital file1 fileBy Soledad Galli

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Feature Selection in Machine Learning

Most machine learning models use too many features. The result: slower training, complex pipelines, and lack of interpretability. This book teaches you how to fix that. Working with Python open-source libraries, you'll master filter, wrapper, embedded, and advanced feature selection methods, understanding how to implement them, when each one earns its place, and exactly what to expect from it.

Digital file2 filesBy Soledad Galli

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Categories

Browse products

Categories

Clustering and Dimensionality Reduction

Forecasting with Machine Learning

Feature Engineering for Time Series Forecasting

Feature Engineering for Machine Learning

Feature Selection for Machine Learning

Machine Learning Interpretability

Hyperparameter Optimization for Machine Learning

Machine Learning with Imbalanced Data

14 Common Feature Engineering Questions - FREE

7 Takes on Working with Imbalanced Data - FREE

Feature Selection in Machine Learning

Clustering and Dimensionality Reduction

Forecasting with Machine Learning

Feature Engineering for Time Series Forecasting

Feature Engineering for Machine Learning

Feature Selection for Machine Learning

Machine Learning Interpretability

Hyperparameter Optimization for Machine Learning

Machine Learning with Imbalanced Data

14 Common Feature Engineering Questions - FREE

7 Takes on Working with Imbalanced Data - FREE

Feature Selection in Machine Learning

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Browse products

Browse products

Browse products

Browse products

Clustering and Dimensionality Reduction

Forecasting with Machine Learning

Feature Engineering for Time Series Forecasting

Feature Engineering for Machine Learning

Feature Selection for Machine Learning

Machine Learning Interpretability

Hyperparameter Optimization for Machine Learning

Machine Learning with Imbalanced Data

14 Common Feature Engineering Questions - FREE

7 Takes on Working with Imbalanced Data - FREE

Feature Selection in Machine Learning

Clustering and Dimensionality Reduction

Forecasting with Machine Learning

Feature Engineering for Time Series Forecasting

Feature Engineering for Machine Learning

Feature Selection for Machine Learning

Machine Learning Interpretability

Hyperparameter Optimization for Machine Learning

Machine Learning with Imbalanced Data

14 Common Feature Engineering Questions - FREE

7 Takes on Working with Imbalanced Data - FREE

Feature Selection in Machine Learning