Technologies / Artificial Intelligence

Machine learning using Random Forest

The training deepens knowledge of the use of the Random Forest algorithm in machine learning tasks. Participants learn the theoretical basis and practical applications of this technique in a variety of classification and regression problems. The program combines theoretical lectures with intensive practical workshops, during which participants implement their own solutions.

Issues

Decision tree theory
Bagging and bootstrapping
Feature importance
Optimization of hyperparameters
Cross-validation
Ensemble methods
Feature engineering
Missing value imputation
Imbalanced learning
Interpretation model
Performance metrics
Error analysis

Benefits

Upon completion of the training, the participant will have a thorough knowledge of the operation and implementation of the Random Forest algorithm
He or she will gain practical skills in selecting and optimizing model parameters for various use cases
He will develop competence in data preparation and feature engineering for ensemble learning models
Will learn to interpret results and evaluate the quality of Random Forest models
Will be able to effectively use Random Forest in real business projects
Will master techniques for dealing with common problems like unbalanced datasets and missing values

Who is this training for?

Data analysts getting started with ensemble learning algorithms

Machine learning engineers looking to expand their knowledge of Random Forest

Python programmers interested in data science

Business professionals working with predictive models

Researchers involved in data analysis

Data science students

ML practitioners looking for effective modeling techniques

Prerequisites

Basic knowledge of statistics and probability
Ability to program in Python
Knowledge of the basics of machine learning
Experience in working with tabular data

Training program

The principle of the Random Forest algorithm
Bagging and bootstrapping

Implementation and tuning
Configuration of model parameters
Validation techniques
Control of overtraining
Optimization of hyperparameters

Feature engineering for Random Forest
Handling missing values

Ensemble methods
Practical applications
Multi-class classification

Anomaly detection
Interpretation of results

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 Machine learning using Random Forest we recommend: Basic knowledge of statistics and probability; Ability to program in Python; Knowledge of the basics of machine learning.

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 getting started with ensemble learning algorithms; Machine learning engineers looking to expand their knowledge of Random Forest; Python programmers interested in data science.