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@@ -74,11 +74,11 @@ A couple of great types of models to start learning with are regression (predict
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The data you need for those is typically very different. Try figuring out why and what kind of models a certain type of data supports well.
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-* CASAS-HAR (Human Activity Recognition from Continuous Ambient Sensor Data) is a very flexible regression dataset.
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-* MNIST handwritten numbers and fashion items datasets are a great start for classification.
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Nowadays, https://www.kaggle.com/datasets[Kaggle] has some great example datasets (requires a free account).
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+* https://casas.wsu.edu/datasets/[CASAS-HAR] (Human Activity Recognition from Continuous Ambient Sensor Data) is a very flexible regression dataset (available for https://www.kaggle.com/datasets/utkarshx27/ambient-sensor-based-human-activity-recognition[download from Kaggle]).
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+* MNIST has two excellent classification datasets: handwritten numbers and fashion items - they are a great start for classification - almost every framework includes them. Both are also available at Kaggle - https://www.kaggle.com/datasets/hojjatk/mnist-dataset[numbers] and https://www.kaggle.com/datasets/zalando-research/fashionmnist[fashion].
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== Getting Running ==
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Create a system-independent Python installation. Trust me, you want to divorce it.
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