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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최근 조회 44,799회
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귀하가 습득할 기술
- Decision Tree
- Ensemble Learning
- Classification Algorithms
- Supervised Learning
- Machine Learning (ML) Algorithms
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
중급 단계
완료하는 데 약 11시간 필요
영어
자막: 영어
제공자:
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
10개 동영상 (총 91분), 6 개의 읽기 자료, 3 개의 테스트
완료하는 데 1시간 필요
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.
완료하는 데 1시간 필요
7개 동영상 (총 50분), 2 개의 읽기 자료, 3 개의 테스트
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
11개 동영상 (총 67분), 2 개의 읽기 자료, 4 개의 테스트
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
8개 동영상 (총 60분), 2 개의 읽기 자료, 3 개의 테스트
완료하는 데 2시간 필요
Ensemble Models
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.
완료하는 데 2시간 필요
15개 동영상 (총 93분), 3 개의 읽기 자료, 6 개의 테스트
완료하는 데 2시간 필요
Modeling Unbalanced Classes
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.
완료하는 데 2시간 필요
6개 동영상 (총 30분), 1 개의 읽기 자료, 3 개의 테스트
검토
SUPERVISED MACHINE LEARNING: CLASSIFICATION의 최상위 리뷰
AP 제공2021년 2월 28일
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
HM 제공2021년 8월 22일
The course content is very great in the coding area and it is very helping. but a shortage that is clear is the theory behind every algorithm, the handling of it wasn't that much perfect.
JM 제공2021년 6월 17일
The course is very well structured, and the explanations very clear. I would only suggest enhancing the peer-review community since it takes a long time to get a review sometimes.
JM 제공2021년 1월 18일
I would like to give especial thanks to the instructor (the one in the videos) for his great job. It would be nice to know who is is.
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