YoVDO

XGBoost in Python from Start to Finish

Offered By: StatQuest with Josh Starmer via YouTube

Tags

XGBoost Courses Machine Learning Courses Python Courses Classification Courses Predictive Modeling Courses Data Formatting Courses Regularization Courses Cross-Validation Courses

Course Description

Overview

Learn how to implement XGBoost in Python from start to finish in this comprehensive 57-minute tutorial. Begin with importing necessary modules and data, then explore techniques for identifying and handling missing data. Dive into data formatting, including creating X and y variables and performing one-hot encoding. Discover how XGBoost handles missing data and one-hot encoded features. Build a preliminary XGBoost model, optimize parameters using cross-validation with GridSearchCV, and finally construct and visualize the final XGBoost model. Gain practical insights into machine learning techniques and boost your data science skills through this hands-on guide.

Syllabus

Awesome song and introduction
Import Modules
Import Data
Missing Data Part 1: Identifying
Missing Data Part 2: Dealing with it
Format Data Part 1: X and y
Format Data Part 2: One-Hot Encoding
XGBoost - Missing Data and One-Hot Encoding
Build a Preliminary XGBoost Model
Optimize Parameters with Cross Validation GridSearchCV
Build and Draw Final XGBoost Model


Taught by

StatQuest with Josh Starmer

Related Courses

Introduction to Artificial Intelligence
Stanford University via Udacity
Natural Language Processing
Columbia University via Coursera
Probabilistic Graphical Models 1: Representation
Stanford University via Coursera
Computer Vision: The Fundamentals
University of California, Berkeley via Coursera
Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent