Decision Trees, Random Forests, AdaBoost & XGBoost in Python [FREE]

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

Decision Trees, Random Forests, 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

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
  • 3 articles
  • 18 downloadable resources
  • Full lifetime access
  • Access on mobile and TV