In my last article of this series, we discussed about the machine learning workflow on the diabetes data set. And discussed about topics such as data exploration, data cleaning, feature engineering basics and model selection process. You can find the previous article below.
Source: towardsdatascience.com
A few examples of images from the Google-Landmarks dataset, including landmarks such as Big Ben, Sacre Coeur Basilica, the rock sculpture of Decebalus and the Megyeri Bridge, among others.
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This article will portray how data related to diabetes can be leveraged to predict if a person has diabetes or not. More specifically, this article will focus on how machine learning can be utilized to predict diseases such as diabetes. By the end of this article series you will be able to understand concepts like data exploration, data cleansing, feature selection, model selection, model evaluation and apply them in a practical way.
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I graduated with a degree in AI when the cost of the equivalent computational power to an iPhone was $50 million. A lot has changed but surprisingly much is still the same.
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