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Monetizing Machine Learning : Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud by Manuel Amunategui and Mehdi Roopaei — free pdf download

Monetizing Machine Learning : Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud by Manuel Amunategui and Mehdi Roopaei.


Monetizing Machine Learning : Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud by Manuel Amunategui and Mehdi Roopaei — free pdf download


This book is for those interested in extending statistical models, machine learning pipelines, data-driven projects, or any stand-alone Python script into interactive dashboards accessible by anyone with a web browser. The Internet is the most powerful medium with an extremely low barrier to entry—anybody can access it, and this book is geared to those who want to leverage that. 

This book assumes you have Python and programming experience. You should have a code editor and interpreter in working order at your disposal. You should have the ability to test and troubleshoot issues, install Python libraries, and be familiar with popular packages such as NumPy, Pandas, and Scikit-learn. An introduction to these basic concepts isn't covered in this book. The code presented here uses Python 3.x only and hasn’t been tested for lower versions. Also, a basic knowledge of web-scripting languages will come in handy. 

This book is geared towards those with an entrepreneurial bent who want to get their ideas onto the Web without breaking the bank, small companies without an IT staff, students wanting exposure and real-world training, and for any data science professional ready to take things to the next level.

How to Use This Book
Each chapter starts with a picture of the final web application along with a description of what it will do. This approach serves multiple purposes:
• It works as a motivator to entice you to put in the work.
• It visually explains what the project is going to be about.
• And more importantly, it teaches how critical it is to have a clear customer-centric understanding of the end game whenever tackling a project.

The book will only show highlights of the source code, but complete versions are attached in the corresponding repositories. This includes a Jupyter notebook when covering data exploration and zipped folders for web applications.

The practical projects presented here are simple, clear, and can be used as templates to jump-start many other types of applications. Whether you want to understand how to create a web application around numerical or categorical predictions, the analysis of text, the creation of powerful and interactive presentations, to offer access to restricted data, or to leverage web plugins to accept subscription payments and donations, this book will help you get your projects into the hands of the world quickly.

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