機器學習 導論

什么是機器學習？ (What is Machine Learning?)

Machine learning can be vaguely defined as a computers ability to learn without being explicitly programmed, this, however, is an older definition of machine learning. A more modern definition was given by Tom Mitchell, "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

可以將機器學習模糊地定義為無需明確編程即可學習的計算機能力，但是，這是機器學習的較早定義。 湯姆·米切爾(Tom Mitchell)給出了一個更現代的定義： “如果某計算機程序在T中的任務上的性能(由P來衡量)隨著經驗的提高而提高，則該計算機程序可以從經驗E中學習一些任務T和性能指標P E。”

For instance, let's assume we have an algorithm that watches emails a user marks as spam and based on that observation it learns to filter out unwanted spam messages. The experience E in the above situation would be to Watch and recognize what type of mail is marked as spam. The task T would be to filter mail as spam based on the experience E. The Performance P would is the efficiency at which the algorithm filters spam mail and it would simply improve with the experience E.

例如，假設我們有一個算法可以監視用戶標記為垃圾郵件的電子郵件，并根據該觀察結果學會過濾掉不需要的垃圾郵件。 在上述情況下的體驗E是觀察并識別哪種類型的郵件被標記為垃圾郵件。 任務T是根據經驗E將郵件過濾為垃圾郵件。性能P將是算法過濾垃圾郵件的效率，并且會隨經驗E的提高而提高。

Machine learning is often confused with Artificial intelligence. Artificial intelligence is measured as the ability of a machine to behave as a human being whereas Machine learning is a subset of artificial intelligence that deals with training a machine or computer to learn from large amounts of data supplied to it.

機器學習通常與人工智能相混淆。 人工智能被衡量為機器表現為人類的能力，而機器學習是人工智能的子集，其處理訓練機器或計算機以從提供給它的大量數據中學習。

Machine learning is implemented in two ways, Supervised and Unsupervised learning.

機器學習有兩種實現方式，監督學習和無監督學習。

Supervised learning is when the machine is given a specific data set along with the correct output. Here the machine is given an idea of what the output must look like with respect to the given input. Supervised learning is further classified into two subsets namely, Regression learning problems and Classification learning problems.

監督學習是指為機器提供特定的數據集以及正確的輸出。 在這里，機器將獲得關于給定輸入的輸出外觀的概念。 監督學習被進一步分為兩個子集，即回歸學習問題和分類學習問題。

In a regression learning problem, we try and obtain predictions as a continuous function of the given input and not as a discrete value whereas in Classification learning problems we try to obtain a discrete value of the output based on previously analyzed data and the given input.

在回歸學習問題中，我們嘗試獲取作為給定輸入的連續函數而不是離散值的預測，而在分類學習問題中，我們嘗試基于先前分析的數據和給定輸入來獲取輸出的離散值。

In classification learning problems, on the other hand, we approach problems without any knowledge about the correct output. The required relationship between the given data and solution can be acquired by clustering the given data based on the relationship of the individual variables present in the given data.

另一方面，在分類學習問題中，我們在沒有任何正確輸出知識的情況下處理問題。 可以通過基于給定數據中存在的各個變量的關系對給定數據進行聚類來獲取給定數據與解決方案之間的所需關系。

Machine learning is used and implemented in various fields of application. Most of us use machine learning algorithms unknowingly in our daily lives. Some of the common applications of machine learning are, Social media services such as personalized social media and news feeds by the content is being searched for, advertisement targetting and product recommendations by monitoring products or services viewed online, email and malware filtering by monitoring the content marked as spam and content classified as malware by users, Refining search engine results to improve search result by monitoring the time spent visiting and viewing web results, personalizing home and voice assistants by monitoring users internet and web activity. Machine learning is an important aspect to predicting highly accurate solutions to problems in various fields of applications such as science, medicine and commerce and can be employed to simplify and improve the quality and rate at which problems are solved.

機器學習在各種應用領域中得到使用和實現。 我們大多數人在日常生活中不知不覺中使用了機器學習算法。 機器學習的一些常見應用包括：社交媒體服務(例如按內容搜索個性化社交媒體和新聞提要)，通過監視在線觀看的產品或服務來確定廣告目標和產品推薦，通過監視內容來進行電子郵件和惡意軟件過濾被用戶標記為垃圾郵件和被用戶歸類為惡意軟件的內容，通過監視訪問和查看Web結果所花費的時間，通過監視用戶的Internet和Web活動來個性化家庭和語音助手來完善搜索引擎結果以改善搜索結果。 機器學習是預測諸如科學，醫學和商業等各種應用領域中的問題的高精度解決方案的重要方面，并且可以用來簡化和提高解決問題的質量和速度。

翻譯自: https://www.includehelp.com/ml-ai/introduction-to-machine-learning.aspx

機器學習 導論

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