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Decision Trees, Random Forests, AdaBoost & XGBoost in Python

Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python.

What you’ll learn:

• Get a solid understanding of the decision tree.
• Understand the business scenarios where the 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 different algorithms.

Requirements:

• Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same.

Who this course is for:

• People pursuing a career in data science.
• Working Professionals beginning their Data journey.
• Statisticians needing more practical experience.
• Anyone curious to master Decision Tree technique from Beginner to Advanced in a short span of time.

This course includes:

• 7 hours of on-demand video.
• 3 articles.
• 19 downloadable resources.
• Full lifetime access.
• Access on mobile and TV.
• Certificate of Completion.

Created by Start-Tech Academy

How to Subscribe For Decision Trees, Random Forests, AdaBoost & XGBoost in Python?

1. Sign Up on Udemy.com
2. Subscribe Here(Decision Trees, Random Forests, AdaBoost & XGBoost in Python): Click Here

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