Machine Learning is the foundation for most AI solutions, and enables the creation of models that predict unknown values and infer insights from observed data.
So how do machines learn?
The answer is, from data. In today's world, we create huge volumes of data as we go about our everyday lives. From the text messages, emails, and social media posts we send to the photographs and videos we take on our phones, we generate massive amounts of information. More data still is created by millions of sensors in our homes, cars, cities, public transport infrastructure, and factories.
Data scientists can use all of that data to train machine learning models that can make predictions and inferences based on the relationships they find in the data.
For example, suppose an environmental conservation organization wants volunteers to identify and catalog different species of wildflower using a phone app. The following animation shows how machine learning can be used to enable this scenario.
A team of botanists and data scientists collects samples of wildflowers.
The team labels the samples with the correct species.
The labeled data is processed using an algorithm that finds relationships between the features of the samples and the labeled species.
The results of the algorithm are encapsulated in a model.
When new samples are found by volunteers, the model can identify the correct species label.
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