Machine Learning with Random Forest Training Course

Last updated

Course Code

randomforest

Duration

14 hours (usually 2 days including breaks)

Requirements

  • An understanding of machine learning concepts
  • Python programming experience

Audience

  • Data scientists
  • Software engineers

Overview

Random Forest is an algorithm for machine learning that is used mostly for classification and regression. It utilizes multiple decision trees to generate more precise and accurate predictions.

This instructor-led, live training (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

By the end of this training, participants will be able to:

  • Set up the necessary development environment to start building machine learning models with Random forest.
  • Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
  • Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
  • Evaluate and optimize machine learning model performance by tuning the hyperparameters.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

  • Overview of Random Forest features and advantages
  • Understanding decision trees and ensemble methods

Getting Started

  • Setting up the libraries (Numpy, Pandas, Matplotlib, etc.)
  • Classification and regression in Random Forests
  • Use cases and examples

Implementing Random Forest

  • Preparing data sets for training
  • Training the machine learning model
  • Evaluating and improving accuracy

Tuning the Hyperparameters in Random Forest

  • Performing cross-validations
  • Random search and Grid search
  • Visualizing training model performance
  • Optimizing hyperparameters

Best Practices and Troubleshooting Tips

Summary and Next Steps

Testimonials

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