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

Description:

You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right?
You've found the right Decision Trees and tree based advanced techniques course!
After completing this course you will be able to:
Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.
Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost
Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result.
Confidently practice, discuss and understand Machine Learning concepts

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