Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Pluralsight

Machine Learning with XGBoost Using scikit-learn in Python

via Pluralsight

Overview

Coursera Plus Annual Sale:
All Certificates & Courses 50% Off!
Grab it


At the core of applied machine learning is supervised machine learning. In this course, Machine Learning with XGBoost Using scikit-learn in Python, you will learn how to build supervised learning models using one of the most accurate algorithms in existence. First, you will discover what XGBoost is and why it’s revolutionized competitive modeling. Next, you will explore the importance of data wrangling and see how clean data affects XGBoost’s performance. Finally, you will learn how to build, train, and score XGBoost models for real-world performance. When you are finished with this course, you will have a foundational knowledge of XGBoost that will help you as you move forward to becoming a machine learning engineer.

Syllabus

  • Course Overview 1min
  • Introducing Essential Processes 18mins
  • Preparing Data for Gradient Boosting 18mins
  • Scoring XGBoost Models 17mins
  • Saving the Trained Model 15mins
  • Selecting Features in Gradient Boosting 21mins

Taught by

Mike West

Reviews

3.8 rating at Pluralsight based on 36 ratings

Start your review of Machine Learning with XGBoost Using scikit-learn in Python

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.