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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Quick Guide: Machine Learning Examples and Uses

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I want to share with you the best real-time examples on machine learning.  Because of new computing technologies, machine learning today is not like machine learning of the past.  While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Machine learning use cases The heavily hyped, self-driving Google car? The essence of machine learning.  Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.  Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.  Fraud detection? One of the more obvious, important uses in our world today. Best example : "pattern recognition" is best example for Machine Learning Related: Take Quiz on Machine Learning Where can you apply machine learning.