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
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約11時間で修了
英語
字幕：英語
提供：
IBM
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シラバス - 本コースの学習内容
2時間で修了
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.
2時間で修了
10件のビデオ （合計91分）, 6 学習用教材, 3 個のテスト
1時間で修了
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.
1時間で修了
7件のビデオ （合計50分）, 2 学習用教材, 3 個のテスト
2時間で修了
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.
2時間で修了
11件のビデオ （合計67分）, 2 学習用教材, 4 個のテスト
2時間で修了
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.
2時間で修了
8件のビデオ （合計60分）, 2 学習用教材, 3 個のテスト
2時間で修了
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.
2時間で修了
15件のビデオ （合計93分）, 3 学習用教材, 6 個のテスト
2時間で修了
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.
2時間で修了
6件のビデオ （合計30分）, 1 学習用教材, 3 個のテスト
レビュー
SUPERVISED MACHINE LEARNING: CLASSIFICATION からの人気レビュー
APにより2021年2月28日
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
JMにより2021年1月18日
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.
AFにより2020年11月7日
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
MBにより2021年4月18日
A well-structured and practical course which helps me answer lots of my concerns from the past until now.
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