AutoML and optimization

Hyperparameter optimization, feature selection, and evolutionary algorithms with Python and scikit-learn.

Connects applied research with the sklearn-genetic-opt open-source library for improving real ML pipelines.

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May 2026 · Machine Learning

Your First Machine Learning Model with Scikit-learn

A step-by-step guide to building your first ML model: data exploration, preprocessing, cross-validation, hyperparameter tuning, and evaluation: all with scikit-learn.

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Apr 2026 · Machine Learning

Adaptive Parameters Methods for Machine Learning

Explore how to use ExponentialAdapter and InverseAdapter in sklearn-genetic-opt to dynamically adjust mutation and crossover probabilities.

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Apr 2026 · Machine Learning

Evolutionary Feature Selection for Machine Learning

Use GAFeatureSelectionCV from sklearn-genetic-opt to find the optimal feature subset using evolutionary algorithms: capturing feature interactions that filter methods miss and automatically determining how many features to keep.

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Mar 2026 · Machine Learning

Are You Still Using Grid Search for Hyperparameters Optimization?

Compare grid search, random search, and evolutionary algorithms for hyperparameter optimization with sklearn-genetic-opt.

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Feb 2026 · Machine Learning

Tune Your Scikit-learn Model Using Evolutionary Algorithms

Hyperparameter tuning with GASearchCV from sklearn-genetic-opt using evolutionary algorithms.

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Are you building an AI or data platform?

I design and build critical systems end to end: architecture, data, models, and product. If that is the scale you are working at, let us talk.

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