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Artificial Intelligence - Machine Learning Process & Companies

Artificial Intelligence - Machine Learning Process & Companies. Prepared on: 1 st May, 2018 By: Celeste Ng Direct quote from source: as shown. Steps in Machine Learning Process. Steps taken by a data scientist in building a machine learning applications 數據科學家在構建機器學習應 用時 採取的步驟.

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Artificial Intelligence - Machine Learning Process & Companies

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  1. Artificial Intelligence - Machine Learning Process & Companies Prepared on: 1st May, 2018 By: Celeste Ng Direct quote from source: as shown

  2. Steps in Machine Learning Process Steps taken by a data scientist in building a machine learning applications數據科學家在構建機器學習應用時採取的步驟 Identify data set確定數據集 2. Select ML algorithm選擇ML算法 3. Develop analytical model開發分析模型 4. Train model訓練模型 5. Generate scores計算分數 Source: https://www.techemergence.com/artificial-intelligence-industry-an-overview-by-segment/

  3. Machine learning algorithms (1)(URL: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/ ) Broadly, there are 3 types of Machine Learning Algorithms.. 1. Supervised Learning監督學習 • How it works: This algorithm consist of a target / outcome variable (or dependent variable)目標/結果變量（或因變量） which is to be predicted from a given set of predictors (independent variables)一組給定的預測變量（獨立變量). Using these set of variables, we generate a function that map inputs to desired outputs生成一個函數將輸入對應的到輸出. The training process continues until the model achieves a desired level of accuracy on the training data. • Examples of Supervised Learning (analytical model): Regression回歸, Decision Tree, Random Forest隨機森林, KNN, Logistic Regression邏輯回歸etc.

  4. Machine learning algorithms (1)(URL: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/ )

  5. Machine learning algorithms (2)(URL: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/ ) 2. Unsupervised Learning無監督學習 • How it works:In this algorithm, we do not have any target or outcome variable to predict / estimate沒有任何目標或結果變量來預測/估計. • It is used for clustering population用於群集人口in different groups, which is widely used for segmenting customers細分客戶in different groups for specific intervention. • Examples of Unsupervised Learning (analytical model): Apriori algorithm, K-means. 3. Reinforcement Learning強化學習: • How it works:  Using this algorithm, the machine is trained to make specific decisions. • It works this way: the machine is exposed to an environment where it trains itself continually using trial and error通過試驗和錯誤不斷訓練自己. • This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions捕捉最佳可能的知識以做出準確的業務決策. • Example of Reinforcement Learning (analytical model): Markov Decision Process馬爾科夫決策過程

  6. Machine learning algorithms (2)(URL: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/ )

  7. Machine Intelligence Companies Source: https://www.techemergence.com/artificial-intelligence-industry-an-overview-by-segment/

  8. Machine Intelligence Companies Source: https://www.techemergence.com/artificial-intelligence-industry-an-overview-by-segment/

  9. Artificial Intelligence Companies • The following AI Sector Map breaks down into 13 broad categories Source: https://www.techemergence.com/artificial-intelligence-industry-an-overview-by-segment/

  10. Artificial Intelligence Market Categories人工智慧市場類別 • Markets and Markets breaks out AI verticals into the following main categories in their 2020 AI Forecast: • Media & advertising • Finance • Retail • Healthcare • Automotive & transportation • Agriculture • Law • Oil & gas • Others Source: https://www.techemergence.com/artificial-intelligence-industry-an-overview-by-segment/

  11. Artificial Intelligence Applications – an example in Taiwan Acer (宏碁) and Taiwan's Center for Disease Control (台灣疾病控制中心) • this forecast platform will serve as an additional source of information … to support their decision making in the • allocation of medical resources in the likelihood of flu epidemics, as well as for the • general public to take extra measures to avoid or protect oneself from areas of flu outbreak. • The flu forecast platform applies • machine learning [AI] on data from the CDC’s flu-like surveillance system and the national health insurance, • together with government information [big data] on weather and regional populations, to establish a prediction model. Source: https://en.ctimes.com.tw/DispNews.asp?O=HK24BBMICJSSAA00NA

  12. Additional note – Company: Appier - https://www.appier.com/en/about.html

  13. Additional note – Setting up chatbot - https://recast.ai/blog/five-steps-bot-building/

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