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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이 강좌에 대하여
귀하가 습득할 기술
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
- Machine Learning (ML) Algorithms
제공자:

IBM
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강의 계획표 - 이 강좌에서 배울 내용
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.
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.
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.
검토
- 5 stars80.76%
- 4 stars15%
- 3 stars3.07%
- 2 stars0.38%
- 1 star0.76%
SUPERVISED MACHINE LEARNING: REGRESSION의 최상위 리뷰
Very well structured course, the explanations were very clear.
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
A well structured course with useful techniques in real life.
Great way learn about machine learning development of regression models
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