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