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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
Languages
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

5. Classification Techniques

Activity 5.01: Ordinary Least Squares Classifier – Binary Classifier

Solution:

  1. Import the required dependencies:
    import struct
    import numpy as np
    import gzip
    import urllib.request
    import matplotlib.pyplot as plt
    from array import array
    from sklearn.linear_model import LinearRegression
  2. Load the MNIST data into memory:
    with gzip.open('../Datasets/train-images-idx3-ubyte.gz', 'rb') as f:
        magic, size, rows, cols = struct.unpack(">IIII", f.read(16))
        img = np.array(array("B", f.read())).reshape((size, rows, cols))
    with gzip.open('../Datasets/train-labels-idx1-ubyte.gz', 'rb') as f:
        magic, size = struct.unpack(">II", f.read(8))
        labels = np.array(array("B", f.read()))
    with gzip.open('../Datasets/t10k-images-idx3-ubyte.gz', 'rb') as f:
     &...
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