Decision Trees, Random Forests, AdaBoost & XGBoost in Python
Decision Trees

Decision Trees, Random Forests, AdaBoost & XGBoost in Python

Description

Decision Trees, Random Forests, AdaBoost & XGBoost in Python is the name of a Udemy training course for learning decision trees using Python. By the end of this course you will be able to identify business problems using Decision tree / Random Forest / XGBoost in machine learning, have a good understanding of advanced decision trees such as Random Forest, Bagging, AdaBoost and XGBoost. You will also be able to build and analyze a decision tree model in Python, and eventually with this course you will be able to understand machine learning concepts, practice them, and also discuss these concepts.

Features of Decision Trees, Random Forests, AdaBoost & XGBoost in Python:

  • Proper understanding of the decision tree
  • Understand business scenarios where the decision tree is applicable.
  • Set up hyperparameters of a machine learning model and increase its efficiency
  • Use Pandas DataFrames to manipulate data and perform statistical calculations
  • Use the decision tree to make predictions
  • Learn the advantages and disadvantages of different algorithms

Course details:

Publisher: Udemy
Instructor: Start-Tech Academy
Language: English
Education Level: Basic to Advanced
Number of Courses: 61
Duration: 7 hours and 8 minutes

Course topics on 11-2020:

Course prerequisites:

Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Installation guide

After Extract, watch with your favorite Player.

English subtitle

Quality: 720

download link

Download Section 1 – 1 GB
Download Part 2 – 890 MB
file password link
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