Ideal for teams that…
Hands-on AI and data analytics workshops — built around your team's real cases.
How to install and configure Scikit-Learn in your work environment
How to build, train, and optimize machine learning models with Scikit-Learn
How to implement advanced models such as decision trees and ensemble methods
How to prepare and deploy Scikit-Learn models in a production environment
What we actually do
- · History and development of Scikit-Learn
- · Main functions and modules of the library
- · Installing Scikit-Learn and dependencies
- · Setting up a working environment (Jupyter Notebook)
- · Data operations: loading, preprocessing, and analysis
- · Preparing data for machine learning models
- · Creating and running basic models (linear regression, classification)
- · Implementing a linear regression model
- · Training and evaluating the model on real data
- · Decision trees and random forests
- · Ensemble models (Boosting, Bagging)
- · Hyperparameter optimization techniques (Grid Search, Random Search)
- · Cross-validation and model evaluation metrics
- · Preparing and processing data for classification
- · Implementing and training models
- · Exporting models and preparing them for deployment
- · Deploying models in a production environment
From brief to retro in 30 days.
Brief & diagnosis
A call with the team lead + a short survey for participants. We define goals, gap and context.
Program customization
We adapt modules, case studies and code examples to your stack. Approval in 5 days.
Workshop
Trainer-led sessions, hands-on, code review. Mentor available between sessions too.
Retro + report
Outcome report for the team and lead. 30 days of consulting included.
Send a brief. We'll reply within 1 day.
After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.
Thank you!
We'll get back to you within 1 business day.
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