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Machine Learning with Swift

You're reading from  Machine Learning with Swift

Product type Book
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Pages 378 pages
Edition 1st Edition
Languages
Authors (3):
Jojo Moolayil Jojo Moolayil
Profile icon Jojo Moolayil
Alexander Sosnovshchenko Alexander Sosnovshchenko
Profile icon Alexander Sosnovshchenko
Oleksandr Baiev Oleksandr Baiev
View More author details

Table of Contents (18) Chapters

Title Page
Packt Upsell
Contributors
Preface
1. Getting Started with Machine Learning 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices Index

Loading the dataset


Create and open a new IPython notebook. In the chapter's supplementary materials, you can see the file extraterrestrials.csv. Copy it to the same folder where you created your notebook. In the first cell of your notebook, execute the magical command:

In []: 
%matplotlib inline 

This is needed to see inline plots right in the notebook in the future.

The library we are using for datasets loading and manipulation is pandas. Let's import it, and load the .csv file:

In []: 
import pandas as pd 
df = pd.read_csv('extraterrestrials.csv', sep='t', encoding='utf-8', index_col=0) 

Object df is a data frame. This is a table-like data structured for efficient manipulations over the different data types. To see what's inside, execute:

In []: 
df.head() 
Out[]: 

Length

Color

Fluffy

Label

0

27.545139

Pink gold

True

Rabbosaurus

1

12.147357

Pink gold

False

Platyhog

2

23.454173

Light black

True

Rabbosaurus

3

29.956698

Pink gold

True

Rabbosaurus

4

34.884065

Light black

True

Rabbosaurus

This prints the first five rows of the...

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