ML with Random Forest Training in Yangon

ML with Random Forest Training in Yangon, Myanmar

Participants will learn how Random Forests work, when to use them, and how to apply them effectively for classification, regression, and feature selection tasks.

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ML with Random Forest Training is a professional training program delivered by ProgNXT in Yangon, Myanmar . ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this ML with Random Forest Training course with hands-on, instructor-led sessions. This hands-on training provides a focused and practical introduction to machine learning using the Random Forest algorithm. Participants will learn...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: IWQ62
14 Hrs
Last Updated: April 2026

Course Rating5/5

This hands-on training provides a focused and practical introduction to machine learning using the Random Forest algorithm. Participants will learn how Random Forests work, when to use them, and how to apply them effectively for classification, regression, and feature selection tasks. The course covers both theoretical understanding and real-world implementation using Python and scikit-learn.

Welcome to the official ML with Random Forest Training certification program in Yangon, Myanmar . This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Basic understanding of machine learning concepts
Familiarity with Python programming
Experience using Jupyter notebooks or similar environments
Basic knowledge of data manipulation using pandas and numpy

By the end of the course, participants will be able to confidently apply Random Forests for various machine learning tasks, evaluate model performance, interpret results, and implement them in production-ready pipelines.

Understand how Random Forest algorithms work
Train and evaluate classification and regression models
Perform feature importance analysis using Random Forests
Tune hyperparameters to optimize model performance
Apply Random Forests to real-world datasets

Ensemble learning principles and decision trees
Random Forest algorithm mechanics and assumptions
Model training, validation, and evaluation metrics
Feature importance, bias-variance tradeoff
Hyperparameter tuning with grid and random search

ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

Foundations and Core Concepts

Introduction to Random Forests

What is Random Forest and how it fits into ML
Use cases: classification, regression, ranking
Comparison with decision trees and other algorithms
Key concepts: bootstrapping, bagging, majority voting

Decision Trees Refresher

Splitting criteria: Gini index, entropy, variance
Overfitting in single trees and motivation for ensembles
Visualizing decision paths and tree depth

Building a Random Forest Model

Constructing forests with scikit-learn
Key parameters: number of trees, max depth, features per split
Handling missing values and categorical data
Out-of-bag (OOB) error and its interpretation

Advanced Topics and Real-World Applications

Regression with Random Forests

Adapting Random Forest for regression problems
Handling continuous outputs and evaluating with RMSE, MAE, R²
Case study: House price or energy consumption prediction
Visualizing prediction intervals and error spread

Feature Importance and Model Interpretability

Feature importance using Gini and permutation methods
Partial dependence plots and SHAP for insights
Dealing with correlated features and dimensionality reduction

Hyperparameter Tuning and Optimization

Grid search vs random search
Key tuning parameters: n_estimators, max_depth, min_samples_split
Cross-validation techniques (k-fold, stratified)
Avoiding overfitting while improving generalization

Final Assessment and Hands-On Exercises

Summary of learnings and real-world deployment tips

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants will be evaluated based on:
Daily quizzes, mock questions and query resolution. Participation in hands-on exercises and labs. Performance in the final review and hands-on exercises will be evaluated.
ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

ML with Random Forest Training Events in yangon

Online Events

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More Upcoming Session Dates

November 2026

📍 Yangon, Myanmar

18th Nov 2026→19th Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→28th Nov 2026

December 2026

📍 Yangon, Myanmar

11th Dec 2026→12th Dec 2026

🌐 Online Instructor-Led

12th Dec 2026→13th Dec 2026

📍 Yangon, Myanmar

19th Dec 2026→20th Dec 2026

🌐 Online Instructor-Led

28th Dec 2026→29th Dec 2026

January 2027

📍 Yangon, Myanmar

6th Jan 2027→7th Jan 2027

🌐 Online Instructor-Led

7th Jan 2027→8th Jan 2027

🌐 Online Instructor-Led

24th Jan 2027→25th Jan 2027

📍 Yangon, Myanmar

26th Jan 2027→27th Jan 2027

Events for other courses in Yangon, Myanmar

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ML with Random Forest Training in Yangon