Technologies / Artificial Intelligence

Feature Engineering in Machine Learning

An advanced training dedicated to the art and science of feature engineering in machine learning projects. The program guides participants through the entire process of creating, selecting, and optimizing features that significantly impact ML model effectiveness. Practical workshops constitute 80% of the training time, during which participants work on diverse datasets, learning to identify and create features relevant to different business problems. The sessions place particular emphasis on understanding the relationships between data nature and selecting appropriate feature engineering techniques.

Why choose this training?

Effective use of modern tools and methodologies, technical competencies have become a critical asset. An advanced training dedicated to the art and science of feature engineering in machine learning projects.

Upon completion, participants will be able to: After completing the training, participants will be able to systematically approach the process of creating and selecting features in machine learning projects, They will gain deep knowledge of how features affect the behavior and effectiveness of ML models, They will develop the ability to identify and create features that significantly improve model results, They will learn to select appropriate data transformation techniques for different types of problems. These competencies directly translate into higher effectiveness in IT projects.

This training is particularly valuable for: Data scientists working on ML model optimization, Machine learning engineers seeking better model effectiveness, Data analysts wanting to deepen their knowledge of data preparation.

What makes our approach unique?

The EITT approach is based on hands-on experience and practical exercises. Over 2 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but practical application skills.

With over 2,500 trainings in our portfolio and a 4.8/5 participant rating, EITT is a trusted partner in IT competency development for organizations of all sizes. Our trainers are experienced practitioners who share up-to-date knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

After completing the training, participants will be able to systematically approach the process of creating and selecting features in machine learning projects
They will gain deep knowledge of how features affect the behavior and effectiveness of ML models
They will develop the ability to identify and create features that significantly improve model results
They will learn to select appropriate data transformation techniques for different types of problems
They will be able to assess the quality and stability of created features over time
They will gain practical experience in automating feature engineering processes
They will master methods for monitoring and managing features in production environments

Who is this training for?

Data scientists working on ML model optimization

Machine learning engineers seeking better model effectiveness

Data analysts wanting to deepen their knowledge of data preparation

ML specialists responsible for model quality

AI programmers working with diverse data

Researchers focused on learning algorithm optimization

Data engineers focused on data preparation

ML practitioners seeking advanced modeling techniques

Prerequisites

Practical knowledge of machine learning
Experience working with data in Python
Fundamentals of statistics and data analysis
Familiarity with data preparation processes

Training program

Feature space analysis and understanding
Impact of features on model performance
Feature selection and creation strategies
Data Transformation Techniques

Handling missing and outlier values
Normalization and standardization
Categorical variable encoding
Advanced Feature Engineering Methods
Automated feature generation

Importance-based feature selection

Optimization and Validation

Feature stability testing

Feature engineering process management

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What are the prerequisites for this training?

For Feature Engineering in Machine Learning we recommend: Practical knowledge of machine learning; Experience working with data in Python; Fundamentals of statistics and data analysis.

What is the format and duration of this training?

The training lasts 2 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data scientists working on ML model optimization; Machine learning engineers seeking better model effectiveness; Data analysts wanting to deepen their knowledge of data preparation.