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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
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Machine Learning Tutorial - Part:2

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Machine learning is a branch of artificial intelligence. Using computing, you will design systems. These systems to behave with AI features, from your end, you need to train them. This process is called Machine Learning. Read my  part-1 if you miss it. The life cycle of machine learning Acquisition - Collect the data  Prepare - Data Cleaning and Quality  Process- Run Machine Tools  Report- Present the Results Acquire Data You can acquire data from many sources; it might be data that are held by your organization or open data from the Internet. There might be one data set, or there could be ten or more. Cleaning of Data You must come to accept that data will need to be cleaned and checked for quality before any processing can take place. These processes occur during the prepare phase. Running Machine Learning Scripts The processing phase is where the work gets done. The machine learning routines that you have created perform this phase. Reporting Finally, the
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