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Python Machine Learning (Wiley)

You're reading from   Python Machine Learning (Wiley) Python makes machine learning easy for beginners and experienced developers

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Product type Paperback
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Length 320 pages
Edition 1st Edition
Languages
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Author (1):
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Wei-Meng Lee Wei-Meng Lee
Author Profile Icon Wei-Meng Lee
Wei-Meng Lee
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Toc

Table of Contents (16) Chapters Close

1. Cover
2. Introduction FREE CHAPTER
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index
16. End User License Agreement

Array Assignment

When assigning NumPy arrays, you have to take note of how arrays are assigned. Following are a number of examples to illustrate this.

Copying by Reference

Consider an array named a1:

list1 = [[1,2,3,4], [5,6,7,8]]
a1 = np.array(list1)
print(a1)
'''
[[1 2 3 4]
 [5 6 7 8]]
''' 

When you try to assign a1 to another variable, a2, a copy of the array is created:

a2 = a1    # creates a copy by reference
print(a1)
'''
[[1 2 3 4]
 [5 6 7 8]]
'''
 
print(a2)
'''
[[1 2 3 4]
 [5 6 7 8]]
''' 

However, a2 is actually pointing to the original a1. So, any changes made to either array will affect the other as follows:

a2[0][0] = 11      # make some changes to a2
print(a1)          # affects a1
'''
[[11  2  3  4]
 [ 5  6  7  8]]
'''
 
print(a2)
'''
[[11  2  3  4]
 [ 5  6  7  8]]
''' 
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