Mlops
Human Face Detection in Images using Machine Learning
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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.
Detecting Plagiarism in Documents
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A system that can automatically detect plagiarism in documents is needed.
Implement a Naive Bayes Classifier
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A Naive Bayes Classifier is a machine learning algorithm that can be used to classify data. It is based on the principle of Bayesian inference, which states that the probability of an event is determined by its prior probability and the evidence for or against it.
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.
Handling Missing Data in Machine Learning
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There are a few different ways to handle missing data when training a machine learning model, including imputation, deletion, and model-based methods.
Testing the accuracy of your spam email predictive model
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To test the accuracy of your spam email predictive model, you can use a holdout dataset or cross-validation. With a holdout dataset, you would split your data into a training set and a test set. You would train your model on the training set and then evaluate it on the test set. With cross-validation, you would split your data into a number of folds . For each fold, you would train your model on the training data and then evaluate it on the test data. You can then average the accuracy of your model across all the folds.
Predicting US House Prices
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The given dataset contains information on house prices in a US city. The task is to use this data to predict the price of a house given its features.
