- Mastering .NET Machine Learning
- Jamie Dixon
- 133字
- 2021-07-09 20:16:37
Why open data?
Many books on machine learning use datasets that come with the language install (such as R or Hadoop) or point to public repositories that have considerable visibility in the data science community. The most common ones are Kaggle (especially the Titanic competition) and the UC Irvine's datasets. While these are great datasets and give a common denominator, this book will expose you to datasets that come from government entities. The notion of getting data from government and hacking for social good is typically called open data. I believe that open data will transform how the government interacts with its citizens and will make government entities more efficient and transparent. Therefore, we will use open datasets in this book and hopefully you will consider helping out with the open data movement.
- Mastering Entity Framework Core 2.0
- 復雜軟件設計之道:領域驅動設計全面解析與實戰
- Python量化投資指南:基礎、數據與實戰
- JavaScript高效圖形編程
- Redis入門指南(第3版)
- 摩登創客:與智能手機和平板電腦共舞
- Building a Game with Unity and Blender
- Photoshop智能手機APP UI設計之道
- JavaScript Unlocked
- Swift Playgrounds少兒趣編程
- 現代C++編程實戰:132個核心技巧示例(原書第2版)
- 深度探索Go語言:對象模型與runtime的原理特性及應用
- Qlik Sense? Cookbook
- Learning iOS Security
- Visual Basic程序設計基礎