summary buyers non-buyers figure the previous figure shows that the data has only two labeled observationsfar too few for supervised learning this figure shows how you can exploit the structure of the underlying data set to learn better classifiers than from the labeled data only the data is split into two clusters by the clustering techniquewe only have two labeled valuesbut if we're bold we can assume others within that cluster have that same label (buyer or non-buyer)as depicted here this technique isn' flawlessit' better to get the actual labels if you can with the basics of machine learning at your disposalthe next discusses using machine learning within the constraints of single computer this tends to be challenging when the data set is too big to load entirely into memory summary in this you learned that data scientists rely heavily on techniques from statistics and machine learning to perform their modeling good number of real-life applications exist for machine learningfrom classifying bird whistling to predicting volcanic eruptions the modeling process consists of four phases feature engineeringdata preparationand model parameterization--we define the input parameters and variables for our model model training--the model is fed with data and it learns the patterns hidden in the data model selection and validation-- model can perform well or poorlybased on its performance we select the model that makes the most sense model scoring--when our model can be trustedit' unleashed on new data if we did our job wellit will provide us with extra insights or give us good prediction of what the future holds