Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python
Sub Category
- Business Analytics & Intelligence
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Objectives
- Get a solid understanding of decision tree
- Understand the business scenarios where decision tree is applicable
- Tune a machine learning model's hyperparameters and evaluate its performance.
- Use Pandas DataFrames to manipulate data and make statistical computations.
- Use decision trees to make predictions
- Learn the advantage and disadvantages of the different algorithms
Pre Requisites
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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