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.
Requirements
- Students will need to install Python
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.
- 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
Why should you choose this course?
And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Who this course is for:
- People pursuing a career in data science
- Working Professionals beginning their Data journey
- Statisticians needing more practical experience