Machine Learning Advanced: Decision Trees in Python
Decision trees are supervised learning algorithms used for both, classification and regression tasks where we will concentrate on classification in this first part of ...HOT & NEW
16 students enrolled
Created by Start-Tech Academy
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
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