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Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
As AI integration across industries picks greater pace, ML engineers are confronted with a sad reality - once stakeholders identify a use case with proven ROI, they are eager to jump onto the AI ship, and data curation is not given its due importance. In a survey of 150 machine learning engineers at large companies, 41 percent say their data is too siloed.
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Testing data is used to test the validity of the training data set. Training data is not used for testing because it will produce the expected output. The testing data set comprises of 20 percent of the total data.
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The process of curating datasets for machine learning starts well before availing datasets. Here’s what we suggest:
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If you have a small dataset, using a model pre-trained on large datasets can be a good idea. You can use your small dataset to fine-tune it.
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Data is the new oil - and just as oil needs the right refining to come into perfect usage, data too needs curing. The power of your machine learning models will greatly depend on the quality of your data.
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ML engineers depend on data during each step of their AI journey – from model selection, training, and tuning to testing. These datasets usually fall under three categories:
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The training data set is used to train an algorithm, apply concepts, learn, and give results. Around 60 percent of data is training data.
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