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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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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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There are different 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. Read CSV file data in chunk size. Or add more RAM (or use powerful server hardware), if you want to continue ...


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You can do this similar to the BIDE approach. It can be done like this: class TreeNode: def __init__(self, element, depth, count=0, parent=None): self.count= count self.element= element self.depth= depth self.subnodes= dict() self.parent= parent def __repr__(self): return f'{self.__class__....


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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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During my master thesis I struggled with this issue. In strict sense, a real AI would be able to say, my sensors (or some of them) are gathering data i cannot understand (yet), and would ideally have a routine what to do whith such cases and ideally one day, even understand that data. But it would most probably now, if that is happening outside in the real ...


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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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