Free Certification Course Title: 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
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
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
This course includes:
7 hours on-demand video
18 downloadable resources
Full lifetime access
Access on mobile and TV
How to Subscribe for Decision Trees, Random Forests, AdaBoost & XGBoost in Python?
- Sign Up on Udemy.com
- Subscribe Here(Decision Trees, Random Forests, AdaBoost & XGBoost in Python): Click Here
Apply Coupon Code: PLSSMA
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