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Machine Learning for Developers

You're reading from   Machine Learning for Developers Uplift your regular applications with the power of statistics, analytics, and machine learning

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781786469878
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Md Mahmudul Hasan Md Mahmudul Hasan
Author Profile Icon Md Mahmudul Hasan
Md Mahmudul Hasan
Rodolfo Bonnin Rodolfo Bonnin
Author Profile Icon Rodolfo Bonnin
Rodolfo Bonnin
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction - Machine Learning and Statistical Science 2. The Learning Process FREE CHAPTER 3. Clustering 4. Linear and Logistic Regression 5. Neural Networks 6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Recent Models and Developments 9. Software Installation and Configuration

K-NN sample implementation

For this simple implementation of the K-NN method, we will use the NumPy and Matplotlib libraries. Also, as we will be generating a synthetic dataset for better comprehension, we will use the make_blobs method from scikit-learn, which will generate well-defined and separated groups of information so we have a sure reference for our implementation.

Importing the required libraries:

    import numpy as np

import matplotlib
import matplotlib.pyplot as plt

from sklearn.datasets.samples_generator import make_blobs
%matplotlib inline

So, it's time to generate the data samples for this example. The parameters of make_blobs are the number of samples, the number of features or dimensions, the quantity of centers or groups, whether the samples have to be shuffled, and the standard deviation of the cluster, to control how dispersed...

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