scikit-learn
The reference Python toolkit for classical machine learning.
scikit-learn covers classification, regression, clustering, dimensionality reduction, model selection and preprocessing, with a consistent fit/predict API across every algorithm. Built on NumPy and SciPy, it's the right choice for tabular data when deep learning is overkill and you want reliable models that are fast to train and simple to ship to production.
What scikit-learn brings to your project.
Typical use cases: Scoring, customer segmentation and prediction on tabular data.
- 01
Uniform fit/predict/transform API across dozens of algorithms.
- 02
Pipelines and cross-validation for a reproducible ML workflow.
- 03
Model selection: grid/random search, metrics, calibration.
- 04
Open source (BSD), outstanding docs, NumPy/SciPy ecosystem.
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Discover our services, expertise and offerings.
Benefit from comprehensive, multidisciplinary expertise to support you through the design, development and maintenance of our applications.
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We use artificial intelligence as a lever for acceleration and optimisation within our technical processes, while keeping strong human oversight on every strategic phase of the project.
AI improves productivity. It does not replace:
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Beyond visual and technical design, as well as AI-augmented programming, our teams act as a layer of validation, quality control, security hardening and steering, to ensure reliable, scalable deliverables that can operate in real-world environments.
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Your company may qualify for a tax credit equal to 20% of the costs linked to developing its solution.
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