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Supervised Learning Models


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Supervised learning is one of the most popular techniques in machine learning. In this course, you will learn about more complicated supervised learning models and how to use them to solve problems.

Target Audience
Anyone interested in understanding machine learning and using it to solve problems

Prerequisites
None

Expected Duration (hours)
0.7

Lesson Objectives

Supervised Learning Models

  • start the course
  • describe the difference between classification and regression models and the use for each of them
  • describe how decision trees can be applied to regression problems
  • describe the CART decision tree learning algorithm and how it's different from C4.5
  • describe the random forests machine learning
  • use scikit-learn to build a random forest model in Python
  • describe the logistic regression model
  • use scikit-learn to fit a logistic regression model
  • describe support vector machine models
  • describe how to use kernel methods with support vector machines to model more complex data
  • use scikit-learn to train and support vector machines in Python
  • describe the Naïve Bayes classifiers and how to train them
  • use scikit-learn to fit a Naïve Bayes classifier in Python
  • describe different supervised learning models in Python
  • Course Number:
    sd_exml_a02_it_enus

    Expertise Level
    Beginner