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README.md

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-# Graph-Machine-Learning-
-Graph Machine Learning, published by Packt
+# Graph Machine Learning
+
+<a href="https://www.packtpub.com/product/graph-machine-learning/9781800204492"><img src="https://static.packt-cdn.com/products/9781800204492/cover/smaller" height="256px" align="right"></a>
+
+This is the code repository for [Graph Machine Learning](https://www.packtpub.com/product/graph-machine-learning/9781800204492), published by Packt.
+
+**Take graph data to the next level by applying machine learning techniques and algorithms**
+
+## What is this book about?
+Data scientists working with network data will be able to put their knowledge to work with this practical guide to building machine learning algorithms using graph data. The book provides a hands-on approach to implementation and associated methodologies that will have you up and running and productive in no time.
+
+This book covers the following exciting features: <First 5 What you'll learn points>
+* Write Python scripts to extract features from graphs
+* Distinguish between the main graph representation learning techniques
+* Become well-versed with extracting data from social networks, financial transaction systems, and more
+* Implement the main unsupervised and supervised graph embedding techniques
+* Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more
+
+
+If you feel this book is for you, get your [copy](https://www.amazon.com/dp/180020390X) today!
+
+<a href="https://www.packtpub.com/?utm_source=github&utm_medium=banner&utm_campaign=GitHubBanner"><img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" 
+alt="https://www.packtpub.com/" border="5" /></a>
+
+
+## Instructions and Navigations
+All of the code is organized into folders. For example, Chapter02.
+
+The code will look like the following:
+```
+base_classifier = KerasClassifier(model=base_model,\
+                                  clip_values=(min_, max_))
+y_test_mdsample_prob = np.max(y_test_prob[sampl_md_idxs],\
+                                                       axis=1)
+y_test_smsample_prob = np.max(y_test_prob[sampl_sm_idxs],\
+                                                       axis=1)
+```
+
+**Following is what you need for this book:**
+This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
+
+With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
+
+### Software and Hardware List
+
+| Chapter  | Software required                   | OS required                        |
+| -------- | ------------------------------------| -----------------------------------|
+| 1        | R version 3.3.0                     | Windows, Mac OS X, and Linux (Any) |
+| 2        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 3        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 4        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 5        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 6        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 7        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 8        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 9        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 10        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 11        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 12        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 13        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 14        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+| 15        | Rstudio Desktop 0.99.903            | Windows, Mac OS X, and Linux (Any) |
+
+
+We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://static.packt-cdn.com/downloads/9781800203907_ColorImages.pdf).
+
+### Related products <Other books you may enjoy>
+* Linux: Powerful Server Administration [[Packt]](https://www.packtpub.com/networking-and-servers/linux-powerful-server-administration?utm_source=github&utm_medium=repository&utm_campaign=9781788293778) [[Amazon]](https://www.amazon.com/dp/1788293770)
+
+* Linux Device Drivers Development [[Packt]](https://www.packtpub.com/networking-and-servers/linux-device-drivers-development?utm_source=github&utm_medium=repository&utm_campaign=9781785280009) [[Amazon]](https://www.amazon.com/dp/1788293770)
+
+## Get to Know the Authors
+**Serg Masís**
+has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making — and machine learning interpretation helps bridge this gap more robustly.