Training: Scikit-Learn
Level
IntermediateDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: Scikit-Learn
The Scikit-Learn training is an intensive two-day course where 80% of the time is dedicated to practical workshops and 20% to theory. The course aims to provide participants with a strong theoretical foundation and practical skills in using Scikit-Learn, a popular machine learning library in Python. Participants will work with real-world data, prepare datasets, build and train models, and learn how to apply their knowledge in real projects.
What will you learn?
- 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
Required technical skills
- Basic knowledge of Python programming
- Basic knowledge of machine learning
- Ability to work in Jupyter Notebook or Google Colab environments
Who is this training for?
Developers and data engineers who want to expand their skills with Scikit-Learn-
Data analysts who want to apply Scikit-Learn in their projects
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AI and machine learning enthusiasts who want to start working with Scikit-Learn
Training Program
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Day 1: Introduction to Scikit-Learn and Machine Learning Basics
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Introduction to Scikit-Learn
- History and development of Scikit-Learn
- Main functions and modules of the library
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Installing and configuring the environment
- Installing Scikit-Learn and dependencies
- Setting up a working environment (Jupyter Notebook)
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Machine learning basics with Scikit-Learn
- Data operations: loading, preprocessing, and analysis
- Preparing data for machine learning models
- Creating and running basic models (linear regression, classification)
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Workshop: Building your first model
- Implementing a linear regression model
- Training and evaluating the model on real data
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Day 2: More Advanced Techniques and Practical Applications
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Other models in Scikit-Learn
- Decision trees and random forests
- Ensemble models (Boosting, Bagging)
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Model optimization and tuning
- Hyperparameter optimization techniques (Grid Search, Random Search)
- Cross-validation and model evaluation metrics
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Workshop: Classification and regression tasks
- Preparing and processing data for classification
- Implementing and training models
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Deploying Scikit-Learn models
- Exporting models and preparing them for deployment
- Deploying models in a production environment
