XGBoost Fun Tutorial | Beginner to Advanced | Boosted Trees, Distributed Training & Advanced Feature

Опубликовано: 22 Август 2025
на канале: Durga Analytics

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Learn *XGBoost from basics to advanced* in this complete 24-chapter tutorial series.
We cover everything from *Boosted Trees, Learning to Rank, DART Booster, Constraints* to **Distributed Training (Kubernetes, Spark, Dask, Ray)**, and **advanced topics like Custom Objectives & Privacy Preserving Inference**.

Perfect for *data scientists, ML engineers, and AI enthusiasts* who want to master XGBoost for production-level machine learning.

📌 YouTube Chapters
00:00 01 Introduction to Boosted Trees
05:01 02 Introduction to Model IO
10:50 03 Learning to Rank
16:19 04 DART Booster
20:45 05 Monotonic Constraints
26:18 06 Feature Interaction Constraints
32:21 07 Survival Analysis with Accelerated Failure Time
38:05 08 Categorical Data
44:05 09 Multiple Outputs
49:40 10 Random Forests™ in XGBoost
55:15 11 Distributed XGBoost on Kubernetes
01:02:32 12 Distributed XGBoost with XGBoost4J-Spark
01:09:49 13 Distributed XGBoost with XGBoost4J-Spark-GPU
01:17:00 14 Distributed XGBoost with Dask
01:22:46 15 Distributed XGBoost with PySpark
01:29:08 16 Distributed XGBoost with Ray
01:37:52 17 Using XGBoost External Memory Version
01:45:35 18 C API Tutorial
01:52:09 19 Text Input Format of DMatrix
01:58:48 20 Notes on Parameter Tuning
02:05:27 21 Custom Objective and Evaluation Metric
02:12:44 22 Advanced Usage of Custom Objectives
02:18:19 23 Intercept
02:23:51 24 Privacy Preserving Inference with Concrete ML

🔥 By the end, you’ll have a **solid understanding of XGBoost internals, deployment, and optimization**.

Source
https://xgboost.readthedocs.io/en/sta...

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