Supervised-Machine-Learning-with-Python

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Supervised-Machine-Learning-with-Python

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Supervised Machine Learning with Python, published by Packt

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# Supervised Machine Learning with Python

This is the code repository for [Supervised Machine Learning with Python](https://www2.packtpub.com/big-data-and-business-intelligence/supervised-machine-learning-python?utm_source=github&utm_medium=repository&utm_campaign=9781838825669), published by Packt.

**Develop rich Python coding practices while exploring supervised machine learning**

## What is this book about?
Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine "learns" under the hood.

This book covers the following exciting features:
* Crack how a machine learns a concept and generalize its understanding to new data
* Uncover the fundamental differences between parametric and non-parametric models
* Implement and grok several well-known supervised learning algorithms from scratch
* Work with models in domains such as ecommerce and marketing
* Expand your expertise and use various algorithms such as regression, decision trees, and clustering

If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1838825665) today!

## Instructions and Navigations

The code will look like the following:
```
from urllib.request import urlretrieve, ProxyHandler, build_opener, install_opener
import requests
import os
pfx = "https://archive.ics.uci.edu/ml/machine-learning databases/spambase/"
data_dir = "data"
```

**Following is what you need for this book:**
This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.

With the following software and hardware list you can run all code files present in the book (Chapter 1-4).

### Software and Hardware List

| Chapter  | Software required                   | OS required                        |
| -------- | ------------------------------------| -----------------------------------|
| 1-4      | Jupyter Notebook Anaconda Python    | Linux                              |

We also provide a PDF file that has color images of the screenshots/diagrams used in this book.  https://www.packtpub.com/sites/default/files/downloads/9781838825669_ColorImages.pdf.

### Related products <Other books you may enjoy>
* Hands-On Unsupervised Learning with Python [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/hands-unsupervised-learning-python?utm_source=github&utm_medium=repository&utm_campaign=9781789348279) [[Amazon]](https://www.amazon.com/dp/1789348277)

* Python Machine Learning By Example - Second Edition [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning-example-second-edition?utm_source=github&utm_medium=repository&utm_campaign=9781789616729) [[Amazon]](https://www.amazon.com/dp/1789616727)

## Get to Know the Author
**Taylor Smith** is a machine learning enthusiast with over five years of experience who loves to apply interesting computational solutions to challenging business problems. Currently working as a principal data scientist, Taylor is also an active open source contributor and staunch Pythonista.
Bio

## Other books by the author
* [Hands-On Machine Learning with Python and Scikit-Learn](https://www.packtpub.com/big-data-and-business-intelligence/hands-machine-learning-python-and-scikit-learn-video?utm_source=github&utm_medium=repository&utm_campaign=9781788991056)

### Suggestions and Feedback
[Click here](https://docs.google.com/forms/d/e/1FAIpQLSdy7dATC6QmEL81FIUuymZ0Wy9vH1jHkvpY57OiMeKGqib_Ow/viewform) if you have any feedback or suggestions.
### Download a free PDF

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF.
 <a href="https://packt.link/free-ebook/9781838825669">https://packt.link/free-ebook/9781838825669 </a>

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