XGBoost for Business: Machine Learning Course in Python & R - Gain Practical Skills for Business Applications of Machine Learning with XGBoost in Python & R by solving a Case Study
XGBoost is an open-source software library that provides a gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala1. It is an efficient implementation of the stochastic gradient boosting algorithm1. XGBoost has been widely used in machine learning competitions and has been shown to be effective in many domains1.
There are many courses available online that teach XGBoost for business purposes. One such course is available on Udemy called “XGBoost for Business: Machine Learning Course in Python & R” by Jose Portilla2. This course covers topics such as XGBoost fundamentals, regression models, classification models, and more.
Another course available on DataCamp called “Extreme Gradient Boosting with XGBoost Course” teaches how to use XGBoost alongside pandas and scikit-learn to build and tune supervised learning models1.
What you'll learn
- Understand the underlying concepts of XGBoost.
- Code in Python and R to implement XGBoost.
- Apply XGBoost to a business problem in the form of a case study.
- Utilize XGBoost to solve similar business problems in the future.
- Understand how to effectively communicate the results of using XGBoost to stakeholders.
- Enhance your skills in coding and machine learning through hands-on practice with XGBoost.
- Understand the role of machine learning in business and how it can be used to improve decision-making and solve complex problems.
- Use machine learning techniques, including XGBoost, to analyze and interpret data in the context of business applications.
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