Scikit-Learn Training

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

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 solid theoretical foundations and practical skills in using Scikit-Learn, a popular machine learning library in Python. Participants will work with real data, prepare data, build and train models, and learn how to apply the acquired knowledge in their projects.

• Programmers and data engineers who want to expand their skills with Scikit-Learn.
• Data analysts wishing to apply Scikit-Learn in their projects.
• Artificial intelligence and machine learning enthusiasts looking to start working with Scikit-Learn.
• Individuals with basic knowledge of programming in Python and machine learning.
• Programmers working in a Jupyter Notebook environment.

• How to install and configure Scikit-Learn in your work environment.
• How to build, train, and optimize machine learning models in Scikit-Learn.
• How to implement advanced models, such as decision trees and ensemble models.
• How to prepare and deploy Scikit-Learn models in a production environment.

• Day 1: Introduction to Scikit-Learn and the basics of machine learning
  1.1. Introduction to Scikit-Learn

  • History and development of Scikit-Learn

  • Main features and modules of the library
    1.2. Installation and configuration of the environment

  • Installing Scikit-Learn and dependencies

  • Configuring the work environment (Jupyter Notebook)
    1.3. Basics of machine learning with Scikit-Learn

  • Operations on datasets: loading, processing, and analysis

  • Preparing data for machine learning models

  • Creating and running basic models (linear regression, classification)
    1.4. Workshop: creating the first model and consolidation

  • Implementing a linear regression model

  • Training and evaluating the model on real data

• Day 2: More advanced techniques and practical applications
  2.1. Other models in Scikit-Learn

  • Decision trees and random forests

  • Ensemble models (Boosting, Bagging)
    2.2. Optimization and tuning of models

  • Hyperparameter optimization techniques (Grid Search, Random Search)

  • Cross-validation and model evaluation metrics
    2.3. Workshop: classification and regression issues

  • Preparing and processing data for classification

  • Implementation and training
    2.4. Deploying Scikit-Learn models

  • Exporting models and preparing for deployment

  • Deploying the model in a production environment

16 h/2 days