XGBoost Training

11 Lesson

25 Hours

Add to Wishlist

XGBoost Training Online is designed to help you master one of the most powerful gradient boosting algorithms for machine learning and predictive analytics. This training focuses on practical learning with XGBoost, Python, Scikit-learn, Pandas, NumPy, and Hyperparameter Tuning techniques to help you develop skills in building accurate classification and regression models, performing feature engineering, optimizing model performance, interpreting results, and deploying scalable machine learning solutions for real-world business applications.

Enroll Now

Overview

Prerequisites

Basic knowledge of Python
Understanding of machine learning fundamentals
Knowledge of classification and regression
Basic statistics and probability
Basic linear algebra
Experience with Pandas and NumPy
Familiarity with Scikit-learn
Familiarity with Jupyter Notebook or a Python IDE
Basic knowledge of data preprocessing and feature engineering (recommended)

What You Will Learn

Understand gradient boosting and the XGBoost algorithm
Install and configure XGBoost (eXtreme Gradient Boosting)
Prepare and preprocess datasets
Perform feature engineering and feature selection
Build classification models with XGBoost
Build regression models with XGBoost
Handle missing values and imbalanced datasets
Tune hyperparameters for better model performance
Evaluate models using machine learning metrics
Apply cross-validation techniques
Interpret models using feature importance and SHAP
Build scalable machine learning pipelines
Optimize model performance for real-world datasets
Deploy XGBoost models for production use
Implement end-to-end predictive analytics projects

Key Features

Instructor Led Training: 25 Hrs
1 On 1 Training Option Available
24 X 7 Lifetime Support & Access
Small Batches Upto 10 Participants
Experienced & Professional Trainers
100% Job Assistance

Course Curriculum

Lesson 1 - Introduction to XGBoost

1. What is XGBoost

2. Gradient Boosting Fundamentals

3. Decision Trees and Ensemble Learning

4. XGBoost Architecture

5. XGBoost Use Cases

6. XGBoost vs Random Forest

7. XGBoost vs LightGBM vs CatBoost

Lesson 2 - Environment Setup

1. Installing XGBoost

2. Python Environment and Jupyter Notebook Setup

3. Installing Required Libraries

4. XGBoost Python Package Overview

5. Scikit-learn Estimator Interface

Lesson 3 - Data Preparation

1. Loading Datasets

2. Data Cleaning

3. Handling Missing Values

4. Encoding Categorical Features

5. Feature Scaling (When Required)

6. Train-Test Split

7. Creating DMatrix

8. Supported Data Structures

Lesson 4 - Building XGBoost Models

1. XGBClassifier

2. XGBRegressor

3. Training Models

4. Model Evaluation

5. Classification and Regression Metrics

6. Cross Validation

Lesson 5 - XGBoost Parameters

1. Making Predictions

2. General, Booster, Learning Task and Tree Booster Parameters

3. Learning Rate

4. Maximum Tree Depth

5. Number of Estimators

6. Regularization Parameters

7. Sampling Parameters

8. Parallel Processing

Lesson 6 - Hyperparameter Tuning

1. Grid Search

2. Early Stopping

3. Validation Sets

4. Callback Functions

5. Improving Model Performance

Lesson 7 - Feature Engineering and Model Interpretation

1. Feature Engineering

2. Feature Selection

3. Feature Importance

4. SHAP-based Model Interpretation

5. Understanding Predictions

Lesson 8 - Advanced XGBoost Features

1. Categorical Data Support

2. Monotonic Constraints

3. Feature Interaction Constraints

4. DART Booster

5. Random Forest Mode

6. Learning to Rank

7. Multiple Outputs

Lesson 9 - Model Management

1. Model Saving and Loading

2. Model I/O

3. Prediction

4. Model Slicing

5. Exporting Models

Lesson 10 - Scalable XGBoost

1. GPU Acceleration

2. Distributed XGBoost

3. XGBoost with Dask

4. XGBoost with PySpark

5. XGBoost with Ray

6. External Memory Training

Lesson 11 - End-to-End Projects

1. Customer Churn Prediction

2. Sales Forecasting

3. Credit Risk Classification

4. Fraud Detection

5. House Price Prediction

6. Predictive Analytics Project

Talk To Us

We are happy to help you

1-800-7430-173 (US Toll Free)

+91 7240740740 (India)

Drop Us a Query

Fields marked * are mandatory

Name

Phone Number

Comments

Request For Live Demo Class

Course Fees

Online Class Room Program

US $ 799.00

100% Money Back Guarantee

Duration : 25 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 13 Aug 2026
Weekday Batch 17 Aug 2026
Weekend Batch 15 Aug 2026

1 ON 1 Training

US $ 899.00

100% Money Back Guarantee

Duration : 25 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 13 Aug 2026
Weekday Batch 17 Aug 2026
Weekend Batch 15 Aug 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

XGBoost Certification

Upon successfully completing the XGBoost Training, you will receive an igmGuru Certificate of Completion recognizing your knowledge of XGBoost, gradient boosting, feature engineering, model training, hyperparameter tuning, model evaluation, and predictive analytics.

Reviews

Login

Email ID*

Password*

Forgot Password?

XGBoost Training

Overview

Prerequisites

What You Will Learn

Key Features

Course Curriculum

Lesson 1 - Introduction to XGBoost

Lesson 2 - Environment Setup

Lesson 3 - Data Preparation

Lesson 4 - Building XGBoost Models

Lesson 5 - XGBoost Parameters

Lesson 6 - Hyperparameter Tuning

Lesson 7 - Feature Engineering and Model Interpretation

Lesson 8 - Advanced XGBoost Features

Lesson 9 - Model Management

Lesson 10 - Scalable XGBoost

Lesson 11 - End-to-End Projects

Request For Live Demo Class

Course Fees

Online Class Room Program

Classes Starting From

1 ON 1 Training

Classes Starting From

Corporate Training

Trusted By Top Companies Worldwide

Want to know Today's Offer

XGBoost Certification

Reviews

Login

Don't have an account?

Our Alumni works at