Decision Trees, Random Forests, AdaBoost & XGBoost in Python

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 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

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 short span of time

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