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

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee
View More author details

1. Cover FREE CHAPTER
2. Introduction
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

# Array Math

You can perform array math very easily on NumPy arrays. Consider the following two rank 2 arrays:

````x1 = np.array([[1,2,3],[4,5,6]])`
`y1 = np.array([[7,8,9],[2,3,4]])` ```

To add these two arrays together, you use the `+` operator as follows:

``print(x1 + y1)` `

The result is the addition of each individual element in the two arrays:

````[[ 8 10 12]`
` [ 6  8 10]]` ```

Array math is important, as it can be used to perform vector calculations. A good example is as follows:

````x = np.array([2,3])`
`y = np.array([4,2])`
`z = x + y`
`'''`
`[6 5]`
`'''` ```

Figure 2.5 shows the use of arrays to represent vectors and uses array addition to perform vector addition.

Besides using the `+` operator, you can also use the `np.add()` function to add two arrays:

``np.add(x1,y1)` `

Apart from addition, you can also perform subtraction, multiplication, as well as division with NumPy arrays:

````print(x1 - y1)     # same as np.subtract(x1,y1)`
`''&apos...````
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