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Learn Python Class - Search Learn Python Class

Learn Python Class - Search Learn Python Class
the XGBoost algorithm for imbalanced classification, it is important to test the default XGBoost model and establish a baseline in performance. Although the XGBoost library has its own Python API
by Mike West

What you'll learn

  • You'll be able to add your rankings on Kaggle to your resume
  • You'll be able to take what you've learned in the course and apply it to the real world
  • You'll understand the machine learning workflow
  • You'll learn why a class of models known as gradient boosters have taken over competitive modeling
  • You'll learn how to tune an XGBoost model
  • The majority of the course is programmtic with real-world code samples

 Description

"An in depth course on XGBoost with code, examples and caveats. Very valuable." - Thibaut

"This was a very comprehensive course on the benefits and how to configure the gradient booster XGBoost." Kevin K

"Nice and quick course with concise code examples. I would recommend to someone with a bit of ML experience, not for beginners (as he says in the first lecture)." Alex G.

"Good breadth of coverage on this topic. Good examples and documentation. To elaborate on the who-this-is-for section, if you know machine learning but not XGBoost specifically, this is for you." Louis B

"Great code samples to get started on my own problems. Thanks!" Stephen E.

Welcome to XGBoost Master Class in Python.

My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. I've published over 50 courses and this is 49 on Udemy. If you're interested in learning what the real-world is really like then you're in good hands.

There's a big difference between real world machine learning and what you read everywhere else. This course is going to focus on the real world.  The real world is often referred to as applied machine learning.

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