Ref: https://thenewstack.io/when-holt-winters-is-better-than-machine-learning/Machine Learning (ML) gets a lot of hype, but its classical predecessors are still immensely powerful, especially in the time-series space. Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters are three Classical methods that are not only incredibly popular but are also excellent time-series predictors.
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Q1: When we extract the data and found that there is an error in the data and we disposing the data, do we still count this data as one of the null sample or considering this as an outlier? or just ditch it completely?
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Q1: Could you please give me an example to consider that which model should be applied when we want to predict downtime of the machine, when we have only the data collected from sensors which installed on that machine?
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Q1: Why a finer threshold (0.99) could not find one of the 2-humped camel?
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Q1: How long do you expect the AI to become more commercialized  and hence lowering the price for general company?
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Q1: What is the difference between data mining and machine learning?
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簡單來說，就是協助企業客戶導入AI 技術。根據維基百科上〈Technology evangelist〉條目的定義，技術傳教士熱衷之事，在於嘗試建立起某技術的群眾效應（Critical mass），也就是讓該技術能達到足夠自我維持且持續成長的動能。就表面而言，達到行銷某技術是其任務與目的，然而就本質而言，他們是試著將自己對技術的熱愛、感動、經驗等傳播出去，不僅是讓更多人接受該技術，而是更發自內心地喜愛，甚至讓更多人自發性地將技術再推廣出去。技術傳教士影響著一群人，並讓這群人影響更多的人。必須持續精進技術能力「技術傳教士是有技術能力的人。可是他不是純做工程師，而是到社群跟其他軟體開發者交流、溝通。」
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We use the visual programming toolbox Orange3 to simplify image analysis by integrating deep learning embedding, machine learning procedures, and data visualization.  Orange supports the
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A multi-layer perceptron (MLP) algorithm with backpropagation.Inputs
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Preprocesses corpus with selected methods.Inputs
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