This problem is about using machine learning to detect human faces in images. The data set consists of images, and the task is to classify them into two categories: those that contain a human face , and those that do not.
K-means clustering is a simple and effective way to cluster data points into two groups. This method is especially useful when the data set is not linearly separable.
The dataset contains customer purchase histories. The task is to cluster the customers into groups based on their purchase patterns.
Supervised learning is a type of machine learning that involves using a labeled training dataset to develop a model that can predict the label for new data.
This problem is a supervised learning problem where the goal is to predict the label of a point in 10 dimensions, given a dataset of 100,000 points with labels.
The k-nearest neighbors algorithm is a supervised learning algorithm that can be used for both classification and regression. The algorithm works by finding the k nearest neighbors to a given data point, and then using those neighbors to predict the class or value of the data point.
This problem deals with finding a mathematical function that best describes the relationship between variables in a given set of data points. This is important in machine learning and data science in order to build accurate models .
The dataset contains housing prices in a city. The goal is to predict the price of a new house in the city.