This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
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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：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
- Machine Learning (ML) Algorithms
可灵活调整截止日期
根据您的日程表重置截止日期。
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。
中级
完成时间大约为11 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
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.
授课大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
Introduction to Supervised Machine Learning and Linear Regression
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
完成时间为 2 小时
9 个视频 （总计 77 分钟）, 3 个阅读材料, 3 个测验
完成时间为 4 小时
Data Splits and Cross Validation
There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
完成时间为 4 小时
12 个视频 （总计 116 分钟）, 3 个阅读材料, 4 个测验
完成时间为 5 小时
Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
完成时间为 5 小时
11 个视频 （总计 126 分钟）, 3 个阅读材料, 3 个测验
审阅
- 5 stars80.39%
- 4 stars15.29%
- 3 stars3.13%
- 2 stars0.39%
- 1 star0.78%
来自SUPERVISED MACHINE LEARNING: REGRESSION的热门评论
由 VO 提供Apr 9, 2021
Very well presented. This is without doubt the best series for Machine Learning on Coursera.
由 SP 提供Aug 10, 2021
Well structured course. Concepts are explained clearly with hands on exercises.
由 WZ 提供Jun 5, 2021
best course ever I learned regression and polynomials in a professional way. thank you
由 JM 提供Jun 10, 2021
Very well structured course, the explanations were very clear.
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