4.9
별점
42개의 평가

Mark J Grover +1명의 강사

제공자:

IBM

이 강좌에 대하여

최근 조회 36,604회

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.
By the end of this course you should be able to:
-Differentiate uses and applications of classification and classification ensembles
-Describe and use logistic regression models
-Describe and use decision tree and tree-ensemble models
-Describe and use other ensemble methods for classification
-Use a variety of error metrics to compare and select the classification model that best suits your data
-Use oversampling and undersampling as techniques to handle unbalanced classes in a data set
 
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.
 
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

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

귀하가 습득할 기술

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

강사

강사 평가

4.9/5 (11개의 평가)

Mark J Grover

Digital Content Delivery Lead

IBM Data & AI Learning

21,652명의 학습자

14개의 강좌

Miguel Maldonado

Machine Learning Curriculum Developer

Data and AI Learning

12,859명의 학습자

6개의 강좌

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 계획 - 이 강좌에서 배울 내용

주

주 1

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

주

주 2

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 for Classification5m

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

Introduction to Support Vector Machines3m

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

주

주 3

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

Introduction to Decision Trees8m

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 12m

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

주

주 4

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

4.9

11개의 리뷰

5 stars
90.47%
4 stars
9.52%