Machine Learning
Classifying handwritten digits with machine learning
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This technical problem involves implementing a machine learning algorithm to classify images of handwritten digits. The input is a set of images of handwritten digits and the output is the classification of the images.
Determining linearity in data sets.
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There are many ways to determine if there is a linear relationship between two data sets. One way is to use a scatter plot. If the data points on the scatter plot fall along a line , then there is a linear relationship between the data sets. Another way to determine if there is a linear relationship is to use a correlation coefficient. If the correlation coefficient is close to 1, then there is a linear relationship between the data sets.
Binary Classification Problem: Learn a Function to Map Features to Labels
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In order to learn a function that can map features to labels for a binary classification problem, you need to have a set of training data that includes both the features and the labels. Once you have this data, you can then use a machine learning algorithm to learn the function.
Housing Price Prediction
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This program takes in a dataset of housing prices and predicts the price of a new house given its features.
Implementing a k-nearest neighbors algorithm for regression.
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The k-nearest neighbors algorithm predicts the target value for a new data point by averaging the target values of the k closest training data points.
Find the Closest Pair of Points in 2D Space
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Given a list of points in 2D space, this algorithm finds the closest pair of points. For example, given the input [(1, 3), (2, 5), (3, 7), (4, 2), (5, 4), (6, 6), (7, 8), (8, 1)], the output would be ((1, 3), (4, 2)).
Finding a specific object in a set of images.
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The problem is to find a specific object in a set of images. This can be done by using a machine learning algorithm to learn what the object looks like and then searching for it in the images.
Predicting House Prices Based on Size
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The dataset contains housing prices in a city. The task is to predict the price of a new house in the city, given its size (in square feet).
