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Machine Learning\n\n## Prerequsities\n\n* Probability\n\n* Statistics\n\n* Linear Algebra\n\n* Calculus\n\nTo get a basic understanding on the statistics, you can try [this](https://www.hackerrank.com/domains/tutorials/10-days-of-statistics) course in HackerRank.\n\nAnd, [here](https://github.com/AnuragAnalog/hackerrank/tree/master/tutorial-10-days-of-statistics) is the solutions.\n\n## Datasets\n\n* Random Dataset for Linear regression\n\n## Loss functions\n\n### Mean Squared Error(MSE)\nIt is the average squared error between the actual and the predicted observations, it is also called as Quadratic Loss, L2 Loss.\nThis method penalizes the values which have high error.\n\n### Mean Absolute Error(MAE)\nIt is the average absolute error between the actual and the predicted observations, it is also called as L1 Loss\nThis method is more sensitive to outliers than MSE, this also needs more complex techniques to compute the gradients.\n\n### Mean Bais Error\nIt is just the difference between the actual and the predicted value, we should take a bit care when dealing with positive and negative bais, because they can make the total bais less.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanuraganalog%2Fmachine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanuraganalog%2Fmachine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanuraganalog%2Fmachine-learning/lists"}