This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
Supervised Machine Learning: Classification
This course is part of multiple programs.
Instructors: Mark J Grover
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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 assignments2 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 assignments2 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 assignments2 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 assignments2 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 assignments7 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 assignments1 peer review2 app items
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