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.
- 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
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