- Machine Learning for the Web
- Andrea Isoni
- 183字
- 2021-07-14 10:46:09
When to use machine learning
Machine learning is not magic and it may be not be beneficial to all data-related problems. It is important at the end of this introduction to clarify when machine-learning techniques are extremely useful:
- It is not possible to code the rules: a series of human tasks (to determine if an e-mail is spam or not, for example) cannot be solved effectively using simple rules methods. In fact, multiple factors can affect the solution and if rules depend on a large number of factors it becomes hard for humans to manually implement these rules.
- A solution is not scalable: whenever it is time consuming to manually take decisions on certain data, the machine-learning techniques can scale adequately. For example, a machine-learning algorithm can efficiently go through millions of e-mails and determine if they are spam or not.
However, if it is possible to find a good target prediction, by simply using mathematical rules, computations, or predetermined schemas that can be implemented without needing any data-driven learning, these advanced machine-learning techniques are not necessary (and you should not use them).
推薦閱讀
- Building a RESTful Web Service with Spring
- Python程序設計(第3版)
- Julia Cookbook
- HTML5+CSS3網站設計基礎教程
- Mastering RStudio:Develop,Communicate,and Collaborate with R
- 單片機應用與調試項目教程(C語言版)
- C語言課程設計
- .NET Standard 2.0 Cookbook
- uni-app跨平臺開發與應用從入門到實踐
- Building Slack Bots
- Mastering Embedded Linux Programming
- Drupal 8 Development Cookbook(Second Edition)
- Android技術內幕(系統卷)
- Monitoring Docker
- Developing Java Applications with Spring and Spring Boot