Images are in abundance in our day-to-day life. Robots need computer vision to understand their surroundings. The majority of the posts on social media include pictures. Handwritten documents require image processing to make them consumable by machines. These and many more uses cases are the reason why image processing is an essential competency for machine learning practitioners to master. In this chapter, we learned how to load images and make sense of their pixels. We also learned how to classify images and reduce their dimensions for better visualization and further manipulation.
We used the nearest neighbor algorithm for image classification and regression. This algorithm allowed us to plug our own metrics when needed. We also learned about other algorithms, such as radius neighbors and nearest centroid. The concepts behind these algorithms and their differences are omnipresent in the field of machine learning. Later on, we will see how the clustering and...
