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.
IBM课程信息
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中级
完成时间大约为11 小时
英语（English）
字幕：英语（English）
您将获得的技能
Decision TreeEnsemble LearningClassification AlgorithmsSupervised LearningMachine Learning (ML) Algorithms
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为11 小时
英语（English）
字幕：英语（English）
提供方
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
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.
完成时间为 2 小时
10 个视频 （总计 91 分钟）, 6 个阅读材料, 3 个测验
10 个视频
Welcome1分钟
Optional: How to create a project in IBM Watson Studio5分钟
Introduction: What is Classification?6分钟
Introduction to Logistic Regression2分钟
Classification with Logistic Regression12分钟
Confusion Matrix, Accuracy, Specificity, Precision, and Recall7分钟
Classification Error Metrics: ROC and Precision-Recall Curves10分钟
Logistic Regression Lab - Part 113分钟
Logistic Regression Lab - Part 216分钟
Logistic Regression Lab - Part 313分钟
6 个阅读材料
About this course3分钟
Optional: Introduction to IBM Watson Studio4分钟
Optional: Overview of IBM Watson Studio3分钟
Optional: Download data assets3分钟
Logistic Regression Demo (Activity)10分钟
Summary/Review4分钟
3 个练习
Logistic Regression4分钟
Logistic Regression Demo2分钟
End of Module10分钟
完成时间为 1 小时
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.
完成时间为 1 小时
7 个视频 （总计 50 分钟）, 2 个阅读材料, 3 个测验
7 个视频
K Nearest Neighbors Decision Boundary3分钟
K Nearest Neighbors Distance Measurement8分钟
K Nearest Neighbors with Feature Scaling5分钟
K Nearest Neighbors Notebook - Part 19分钟
K Nearest Neighbors Notebook - Part 26分钟
K Nearest Neighbors Notebook - Part 311分钟
2 个阅读材料
K Nearest Neighbors Demo (Activity)3分钟
Summary/Review1分钟
3 个练习
K Nearest Neighbors3分钟
N Nearest Neighbors Demo5分钟
End of Module15分钟
完成时间为 2 小时
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.
完成时间为 2 小时
11 个视频 （总计 67 分钟）, 2 个阅读材料, 4 个测验
11 个视频
Classification with Support Vector Machines2分钟
The Support Vector Machines Cost Function5分钟
Regularization in Support Vector Machines6分钟
Introduction to Support Vector Machines Gaussian Kernels2分钟
Support Vector Machines Gaussian Kernels - Part 14分钟
Support Vector Machines Gaussian Kernels - Part 24分钟
Implementing Support Vector Machines Kernel Models8分钟
Support Vector Machines Notebook - Part 18分钟
Support Vector Machines Notebook - Part 28分钟
Support Vector Machines Notebook - Part 310分钟
2 个阅读材料
Support Vector Machines Demo (Activity)3分钟
Summary/Review2分钟
4 个练习
Support Vector Machines5分钟
Support Vector Machines Kernels3分钟
Support Vector Machines Demo3分钟
End of Module10分钟
完成时间为 2 小时
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.
完成时间为 2 小时
8 个视频 （总计 60 分钟）, 2 个阅读材料, 3 个测验
8 个视频
Building a Decision Tree6分钟
Entropy-based Splitting2分钟
Other Decision Tree Splitting Criteria4分钟
Pros and Cons of Decision Trees5分钟
Decision Trees Notebook - Part 16分钟
Decision Trees Notebook - Part 28分钟
Decision Trees Notebook - Part 315分钟
2 个阅读材料
Decision Trees Demo (Activity)10分钟
Summary/Review3分钟
3 个练习
Decision Trees4分钟
Decision Trees Demo3分钟
End of Module10分钟
完成时间为 2 小时
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.
完成时间为 2 小时
15 个视频 （总计 93 分钟）, 3 个阅读材料, 6 个测验
15 个视频
Ensemble Based Methods and Bagging - Part 21分钟
Ensemble Based Methods and Bagging - Part 33分钟
Random Forest7分钟
Bagging Notebook - Part 16分钟
Bagging Notebook - Part 26分钟
Bagging Notebook - Part 39分钟
Review of Bagging4分钟
Overview of Boosting3分钟
Adaboost and Gradient Boosting Overview7分钟
Adaboost and Gradient Boosting Syntax4分钟
Stacking7分钟
Boosting Notebook - Part 17分钟
Boosting Notebook - Part 215分钟
Boosting Notebook - Part 35分钟
3 个阅读材料
Bagging Demo (Activity)3分钟
Boosting and Stacking Demo (Activity)3分钟
Summary/Review10分钟
6 个练习
Bagging5分钟
Random Forest3分钟
Bagging Demo3分钟
Boosting and Stacking5分钟
Boosting and Stacking Demo5分钟
End of Module10分钟
完成时间为 2 小时
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.
完成时间为 2 小时
6 个视频 （总计 30 分钟）, 1 个阅读材料, 3 个测验
6 个视频
Upsampling and Downsampling6分钟
Modeling Approaches: Weighting and Stratified Sampling3分钟
Modeling Approaches: Random and Synthetic Oversampling5分钟
Modeling Approaches: Nearing Neighbor Methods4分钟
Modeling Approaches: Blagging5分钟
1 个阅读材料
Summary/Review10分钟
2 个练习
Modeling Unbalanced Classes4分钟
End of Module10分钟
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