XGBoost Courses and Certifications

Opioid Use Disorder Prediction Using AI and Existing Risk Models

Stanford University offers a short study on using AI for predicting Opioid Use Disorder, demonstrating its effectiveness over traditional clinical tools.

• YouTube
• 52 minutes
• On-Demand
• Free Online Course

Trees, SVM and Unsupervised Learning

Learn SVMs, neural networks, decision trees, and XG boost in a 4-week course by University of Colorado Boulder. Gain hands-on experience in building predictive models and data representation.

• Coursera
• 12 hours 58 minutes
• On-Demand
• Free Online Course (Audit)

Machine Learning using Python

367 ratings at Udemy

Master machine learning techniques with Python in this 19-hour course. Learn to build predictive models, solve real-life problems, and convert business issues into machine learning tasks.

• Udemy
• 18 hours 49 minutes
• On-Demand
• Paid Course

Complete Machine Learning with R Studio - ML for 2024

2453 ratings at Udemy

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio

• Udemy
• 12 hours
• On-Demand
• Paid Course

Modern Artificial Intelligence Masterclass: Build 6 Projects

1017 ratings at Udemy

Harness the power of AI to solve practical, real-world problems in Finance, Tech, Art and Healthcare

• Udemy
• 15 hours 48 minutes
• On-Demand
• Paid Course

Decision Trees, Random Forests, AdaBoost & XGBoost in Python

898 ratings at Udemy

Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python

• Udemy
• 7 hours 19 minutes
• On-Demand
• Paid Course

Practical Machine Learning with Scikit-Learn

307 ratings at Udemy

Learn powerful machine learning algorithms and data preprocessing in under an hour, including regression, classification, and boosting, using Python's Scikit-Learn library.

• Udemy
• 1 hour 7 minutes
• On-Demand
• Free Online Course

Decision Trees, Random Forests, Bagging & XGBoost: R Studio

217 ratings at Udemy

Decision Trees and Ensembling techinques in R studio. Bagging, Random Forest, GBM, AdaBoost & XGBoost in R programming

• Udemy
• 5 hours 53 minutes
• On-Demand
• Paid Course

2024 Python for Machine Learning: A Step-by-Step Guide

110 ratings at Udemy

Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc

• Udemy
• 1 day 8 hours 5 minutes
• On-Demand
• Paid Course

XGBoost

XGBoost in Python from Start to Finish. Part 1 (of 4): Regression. Part 2 ( of 4): Classification. Part 3 (of 3): Mathematical Details. Part 4 (of four): Crazy Cool Optimizations. Part 5 (of 5) : Mathematical details. Part 6 (of 6) : Math details.

• YouTube
• 2 hours 30 minutes
• On-Demand
• Free Online Course

Credit Risk Modeling in Python

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Microcredential

AWS Machine Learning Engineer Nanodegree

41 ratings at Udacity

Master job-ready skills in machine learning engineering with this 21-week course. Learn to create ML workflows, train deep learning models, and operationalize ML projects using AWS SageMaker.

• Udacity
• 6 months
• On-Demand
• Paid Course

Extreme Gradient Boosting with XGBoost

29 ratings at DataCamp

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Machine Learning with XGBoost Using scikit-learn in Python

26 ratings at Pluralsight

XGBoost is the most winning supervised machine learning approach in competitive modeling on structured datasets. This course will teach you the basics of XGBoost, including basic syntax, functions, and implementing the model in the real world.

• Pluralsight
• 1-2 hours
• On-Demand
• Free Trial Available

Applied Classification with XGBoost

19 ratings at Pluralsight

Using Jupyter notebook demos, you'll experience preliminary exploratory data analysis. You will create a classification model with XGBoost. Using third-party libraries, you will explore feature interactions, and explaining the models.

• Pluralsight
• 2 hours 3 minutes
• On-Demand
• Free Trial Available