IBM
Supervised Machine Learning: Classification
IBM

Supervised Machine Learning: Classification

This course is part of multiple programs.

Taught in English

Some content may not be translated

Mark J Grover
Svitlana (Lana) Kramar
Joseph Santarcangelo

Instructors: Mark J Grover

28,935 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(322 reviews)

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95%

Intermediate level
Some related experience required
24 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

22 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(322 reviews)

|

95%

Intermediate level
Some related experience required
24 hours (approximately)
Flexible schedule
Learn at your own pace

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There are 6 modules in this course

Logistic regression is one of the most studied and widely used classification algorithms, probably due to its popularity in regulated industries and financial settings. Although more modern classifiers might likely output models with higher accuracy, logistic regressions are great baseline models due to their high interpretability and parametric nature. This module will walk you through extending a linear regression example into a logistic regression, as well as the most common error metrics that you might want to use to compare several classifiers and select that best suits your business problem.

What's included

12 videos4 readings3 quizzes2 app items

K Nearest Neighbors is a popular classification method because they are easy computation and easy to interpret. This module walks you through the theory behind k nearest neighbors as well as a demo for you to practice building k nearest neighbors models with sklearn.

What's included

8 videos1 reading3 quizzes2 app items

This module will walk you through the main idea of how support vector machines construct hyperplanes to map your data into regions that concentrate a majority of data points of a certain class. Although support vector machines are widely used for regression, outlier detection, and classification, this module will focus on the latter.

What's included

12 videos1 reading4 quizzes2 app items

Decision tree methods are a common baseline model for classification tasks due to their visual appeal and high interpretability. This module walks you through the theory behind decision trees and a few hands-on examples of building decision tree models for classification. You will realize the main pros and cons of these techniques. This background will be useful when you are presented with decision tree ensembles in the next module.

What's included

9 videos2 readings3 quizzes2 app items

Ensemble models are a very popular technique as they can assist your models be more resistant to outliers and have better chances at generalizing with future data. They also gained popularity after several ensembles helped people win prediction competitions. Recently, stochastic gradient boosting became a go-to candidate model for many data scientists. This model walks you through the theory behind ensemble models and popular tree-based ensembles.

What's included

15 videos3 readings6 quizzes7 app items

Some classification models are better suited than others to outliers, low occurrence of a class, or rare events. The most common methods to add robustness to a classifier are related to stratified sampling to re-balance the training data. This module will walk you through both stratified sampling methods and more novel approaches to model data sets with unbalanced classes. 

What's included

10 videos1 reading3 quizzes1 peer review2 app items

Instructors

Instructor ratings
4.8 (100 ratings)
Mark J Grover
IBM
13 Courses89,017 learners
Svitlana (Lana) Kramar
IBM
3 Courses106,575 learners
Joseph Santarcangelo
IBM
28 Courses1,435,709 learners

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IBM

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4.8

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