Technologies / Artificial Intelligence

Fraud detection with Python and TensorFlow

The training is devoted to the practical use of machine learning techniques in fraud and anomaly detection using Python and TensorFlow. Participants will learn advanced methods for analyzing data and building predictive models, with a special focus on the specifics of unbalanced data. The program is delivered in a workshop format, where theory is immediately translated into practical implementations using real use cases.

Issues

Sampling methods for unbalanced data
Data augmentation techniques
Neural network architecture in anomaly detection
Ensemble learning in fraud detection
Evaluation metrics for unbalanced problems
Interpretation of machine learning models
Real-time processing of data streams
Strategies for updating models
Techniques for reducing false positives
Monitoring system performance
Fraud detection pipeline
Integration with production systems

Benefits

The participant will gain practical skills in designing and implementing fraud detection systems using machine learning
He or she will learn to work effectively with unbalanced data and select appropriate sampling techniques
Will develop the ability to implement advanced anomaly detection models in the TensorFlow environment
Will learn methods for evaluating and interpreting the results of fraud detection models
Will gain knowledge of best practices for implementing and maintaining fraud detection systems in a production environment

Who is this training for?

Data analysts working in the financial sector

Information systems security specialists

Python programmers interested in machine learning

Data engineers involved in anomaly detection

Risk management experts

Employees of compliance and audit departments

Anti-fraud specialists

Prerequisites

Knowledge of the basics of machine learning
Experience in Python programming
Basic knowledge of data analysis
Knowledge of statistics and probability

Training program

Specifics of unbalanced data

Data sampling and augmentation techniques
Construction of anomaly detection models
Implementation of anomaly detectors
Neural networks in fraud detection

Ensemble models in unbalanced problems
Advanced techniques and optimization
Selection of appropriate evaluation metrics
Training strategies for unbalanced data
Techniques for interpreting the results

System implementation and maintenance
Building a fraud detection pipeline

Support for alerts and false positives
Integration with production systems

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

Frequently asked questions

What are the prerequisites for this training?

For Fraud detection with Python and TensorFlow we recommend: Knowledge of the basics of machine learning; Experience in Python programming; Basic knowledge of 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 analysts working in the financial sector; Information systems security specialists; Python programmers interested in machine learning.