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Before jumping to modeling, there are a few tasks a data scientist (or ML/AI practitioner) must do: Ideation (or hypothesizing): Before applying any modeling approach, we need to ask the right questions. We must clearly mention our assumptions and declare how we want to measure the effectiveness of the pipeline. Note that, some tools/algorithms might not ...


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Is your question about storing, writing, or reading/processing huge data? I'm not an expert in this topic, but I know a couple of possible ways to handle huge datasets: If the data is too big to be fully uploaded to RAM, you can iterate over it in Pandas. You can find a brief explanation in the article Why and How to Use Pandas with Large Data, section 1. ...


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Terms in a field are sometimes defined unambiguously. For instance, we know what convergence means when communicating about machine learning algorithms in academic publications because it has a formal definition in an older field, mathematics. However, the term machine learning is defined ambiguously across academic publications. Perspectives on Machine ...


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As you are handling with time series data and you want to find trends; A good approach should be consider applying Holt-Winter's seasonal method. This algorithm handle seasonal, trend and smooth parameters. A good implementation of this kind of algorithm is Prophet by Facebook. You can code an exploratory analysis with this library and obtain trend, yearly ...


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In data mining, we can use machine learning (ML) (with the help of unsupervised learning algorithms) to recognize patterns. Pattern recognition is a process of recognizing patterns such as images or speech. We can recognise patterns using ML. For example, once a neural net is trained, using ML algorithms, it can be used for pattern recognition. Other ...


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Machine learning is a form of pattern recognition. Machine learning is basically the idea of training machines to recognize patterns and apply it to particle problems. Data science is the science of apply machine learning to practical problems such as creating better search engine results or classifying images. Patten recognition is pretty much the umbrella ...


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