(Python Example for Beginners)
Write a NumPy program to compute the covariance matrix of two given arrays.
import numpy as np x = np.array([0, 1, 2]) y = np.array([2, 1, 0]) print("nOriginal array1:") print(x) print("nOriginal array1:") print(y) print("nCovariance matrix of the said arrays:n",np.cov(x, y))
Original array1: [0 1 2] Original array1: [2 1 0] Covariance matrix of the said arrays: [[ 1. -1.] [-1. 1.]]
Python Example – Write a NumPy program to compute the weighted of a given array
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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