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Decision trees and churn rate

The course is an invitation to use decision trees for binary classification problems,

The course is divided into classes as follows:
1. What is machine learning and what are decision trees?

2. A look at Python.

3. Formal description of algorithms that learn decision trees
4. Implementation of decision trees for churn rate prediction. 5. Advanced aspects of decision trees (three hours).

Syllabus

  1. Introduction

    • What is Machine Learning and Decision Trees ?

    • Mathematical notation

  2. Python installation and a working environment for MachineLearning

    • Mini-microwave installation

    • Installation for Windows.

    • Installation for MacOS

    • Installation for Linux

    • Creation of a virtual environment for data analysis

    • Disable the base environment

    • Creation of a specific environment for data analysis using conda-forge

  3. Decision trees and their training algorithms

    • Decision trees

    • Learning algorithm and the concept of entropy

  4. Churn rate binary classifier for telecommunications companies

    • Churn rate

    • Database Description

  5. Selected topics in decision trees

    • Motivation of the concept of entropy

    • Gini impurity index

    • Random forests vs over-fit

    • Pruning

Descarga las notas del curso aquí:

Contact

CDMX, México.

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