Ai+ ML

Ai(人工智能)+ ML(機器學習)

Ai is Data 人工智能就是數據


Countless companies are either working on machine learning projects or are dreaming of using the technology as quickly as possible. However, over time, many of these ambitious projects don’t deliver the desired outcome. This is often due to the poor quality of the available data that is fed into the algorithms. “Garbage in, garbage out” is an iron law in the field of machine learning. That’s why data scientists are of crucial importance in any machine learning project. They analyze and clean the data, transform it into the desired format with the required quality.


What is Ai (Artificial Intelligence) / ML (機器學習 (ML))?

Ai vs ML vs Deep Learning 人工學習 vs 機器學習 vs 深度學習

Artificial Learning vs 機器學習 (ML) vs Deep Learning

Artificial Learning vs Machine Learning vs Deep Learning 人工學習 vs 機器學習 vs 深度學習

Artificial Learning, 機器學習 (ML) and Deep Learning Differences

  • A.I人類智能由機器展示。 簡而言之,使用可以模仿人類功能(例如學習和解決問題)的機器。 我 一般情況下,機器會表現得像人一樣,使用算法思維來表現和思考。
  • Artificial intelligence and machine learning are used interchangeably often but they are not the same. Machine learning is one of the most active areas and a way to achieve AI. Why ML is so good today; for this, there are a couple of reasons like below but not limited to though.
  • The explosion of big data
  • 渴望在這個業務不斷縮水的時期內尋求新業務和收入流
  • Advancements in machine learning algorithms
  • 開發功能強大、具有高容量和更快的計算能力的機器
  • 存儲容量
  • MLaaS is needed for data scientist’s work, architects, and data engineers who have domain expertise. Everyone needs to have a better understanding of the possibilities of 機器學習 (ML).

What 機器學習 (ML) can do:


  • 預報
  • 記住
  • 複製
  • 選擇最好的項目


What 機器學習 (ML) can’t do:


  • 創建新的東西
  • 快速變得聰明
  • 超越他們的任務
  • 殺死所有人


Modern Ai Approach 現代人工智能方法

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