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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
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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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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

Creating NumPy Arrays

Before using NumPy, you first need to import the NumPy package (you may use its conventional alias np if you prefer):

import numpy as np 

The first way to make NumPy arrays is to create them intrinsically, using the functions built right into NumPy. First, you can use the arange() function to create an evenly spaced array with a given interval:

a1 = np.arange(10)        # creates a range from 0 to 9
print(a1)                 # [0 1 2 3 4 5 6 7 8 9]
print(a1.shape)           # (10,) 

The preceding statement creates a rank 1 array (one‐dimensional) of ten elements. To get the shape of the array, use the shape property. Think of a1 as a 10×1 matrix.

You can also specify a step in the arange() function. The following code snippet inserts a step value of 2:

a2 = np.arange(0,10,2)    # creates a range from 0 to 9, step 2
print(a2)                 # [0 2 4 6 8] 

To create an array of a specific size filled with 0s, use the zeros() function:

a3 = np.zeros(5...
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