"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.
Important - I want you to take my course, however, I want the course to be right for you. This is NOT an entry level course in machine learning. You'll need have a solid background in core Python. If you haven't taken my courses on data wrangling and core Python for machine learning please do that first.
This is a master class but what does that mean? It simply means the course is more advanced. You'll need to have a solid background in Python and I'd suggest a well rounded background on the fundamentals of machine learning.
In applied machine learning almost all your models will be supervised learning models. That simply means you'll build your models against existing data. That data will be in the shape of an array. Think of an excel spreadsheet. Most models will require your data be in that specific shape before they can model it.
In competitive modeling and the real world, a group of algorithms known as gradient boosters has taken the world be storm. They've won almost every single competition in the structured data category. In this course I'm going to show you how to use them to score high on the world's most competitive machine learning competition. Kaggle.
Here's what you'll learn:
Define gradient boosters
Cleanse your data for success
Build award winning models with XGBoost
Use them on your real world models
Add the ranking to your resume
Competitive modeling tells employers you understand the basics of the machine learning workflow. If you can work through the machine learning workflow from end to end your chances of securing a job in this space are greatly improved.
Make no mistake, the barrier to entry in this space is large. While this is only one step in a long arduous process to becoming a real world machine learning engineer, it's one of the most important things you can do right now to build your skills and your resume.
If you really want to be a part of one the most exciting career paths in the world then take this course now!!!
Who this course is for:
- If you want to become a machine learning engineer then this course is for you.
- If you want something beyond the typical lecture style course then this course is for you.
- If you want to impress perspective employers with your data science acumen this course is for you