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.
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Habilidades que obtendrás
- Decision Tree
- Ensemble Learning
- Classification Algorithms
- Supervised Learning
- Machine Learning (ML) Algorithms
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Habilidades en redes de IBM
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Programa - Qué aprenderás en este curso
Logistic Regression
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.
K Nearest Neighbors
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.
Support Vector Machines
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.
Decision Trees
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.
Reseñas
- 5 stars88,43Â %
- 4 stars10,40Â %
- 3 stars0,57Â %
- 1 star0,57Â %
Principales reseñas sobre SUPERVISED MACHINE LEARNING: CLASSIFICATION
Superb ,detailed, well explained, lots of hands on training through labs and most of the major alogrithms are covered!
Keep up the good work. You guys are helping the community a lot :D
This course is has a detailed explanation on each and every aspect of classification.
Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences. A good understanding of classifiers is gained from this course.
this course taught me a lot even after being a practioner for 10+ years!
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