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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Decision TreeEnsemble LearningClassification AlgorithmsSupervised LearningMachine Learning (ML) Algorithms
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 11시간 필요
영어
자막: 영어
제공자:
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
강의 계획 - 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
10개의 동영상
Welcome1m
Optional: How to create a project in IBM Watson Studio5m
Introduction: What is Classification?6m
Introduction to Logistic Regression2m
Classification with Logistic Regression12m
Confusion Matrix, Accuracy, Specificity, Precision, and Recall7m
Classification Error Metrics: ROC and Precision-Recall Curves10m
Logistic Regression Lab - Part 113m
Logistic Regression Lab - Part 216m
Logistic Regression Lab - Part 313m
6개의 읽기 자료
About this course3m
Optional: Introduction to IBM Watson Studio4m
Optional: Overview of IBM Watson Studio3m
Optional: Download data assets3m
Logistic Regression Demo (Activity)10m
Summary/Review4m
3개 연습문제
Logistic Regression4m
Logistic Regression Demo2m
End of Module10m
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
7개의 동영상
K Nearest Neighbors Decision Boundary3m
K Nearest Neighbors Distance Measurement8m
K Nearest Neighbors with Feature Scaling5m
K Nearest Neighbors Notebook - Part 19m
K Nearest Neighbors Notebook - Part 26m
K Nearest Neighbors Notebook - Part 311m
2개의 읽기 자료
K Nearest Neighbors Demo (Activity)3m
Summary/Review1m
3개 연습문제
K Nearest Neighbors3m
N Nearest Neighbors Demo5m
End of Module15m
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
11개의 동영상
Classification with Support Vector Machines2m
The Support Vector Machines Cost Function5m
Regularization in Support Vector Machines6m
Introduction to Support Vector Machines Gaussian Kernels2m
Support Vector Machines Gaussian Kernels - Part 14m
Support Vector Machines Gaussian Kernels - Part 24m
Implementing Support Vector Machines Kernel Models8m
Support Vector Machines Notebook - Part 18m
Support Vector Machines Notebook - Part 28m
Support Vector Machines Notebook - Part 310m
2개의 읽기 자료
Support Vector Machines Demo (Activity)3m
Summary/Review2m
4개 연습문제
Support Vector Machines5m
Support Vector Machines Kernels3m
Support Vector Machines Demo3m
End of Module10m
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
8개의 동영상
Building a Decision Tree6m
Entropy-based Splitting2m
Other Decision Tree Splitting Criteria4m
Pros and Cons of Decision Trees5m
Decision Trees Notebook - Part 16m
Decision Trees Notebook - Part 28m
Decision Trees Notebook - Part 315m
2개의 읽기 자료
Decision Trees Demo (Activity)10m
Summary/Review3m
3개 연습문제
Decision Trees4m
Decision Trees Demo3m
End of Module10m
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
15개의 동영상
Ensemble Based Methods and Bagging - Part 21m
Ensemble Based Methods and Bagging - Part 33m
Random Forest7m
Bagging Notebook - Part 16m
Bagging Notebook - Part 26m
Bagging Notebook - Part 39m
Review of Bagging4m
Overview of Boosting3m
Adaboost and Gradient Boosting Overview7m
Adaboost and Gradient Boosting Syntax4m
Stacking7m
Boosting Notebook - Part 17m
Boosting Notebook - Part 215m
Boosting Notebook - Part 35m
3개의 읽기 자료
Bagging Demo (Activity)3m
Boosting and Stacking Demo (Activity)3m
Summary/Review10m
6개 연습문제
Bagging5m
Random Forest3m
Bagging Demo3m
Boosting and Stacking5m
Boosting and Stacking Demo5m
End of Module10m
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
6개의 동영상
Upsampling and Downsampling6m
Modeling Approaches: Weighting and Stratified Sampling3m
Modeling Approaches: Random and Synthetic Oversampling5m
Modeling Approaches: Nearing Neighbor Methods4m
Modeling Approaches: Blagging5m
1개의 읽기 자료
Summary/Review10m
2개 연습문제
Modeling Unbalanced Classes4m
End of Module10m
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