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Monetizing Machine Learning in pdf


Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud 1st ed. Edition by Manuel Amunategui , Mehdi Roopaei is a PDF book for free download.

Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book―Amazon, Microsoft, Google, and PythonAnywhere.

You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. 

You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time.

Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book.

What You’ll Learn

Extend your machine learning models using simple techniques to create compelling and interactive web dashboards

Leverage the Flask web framework for rapid prototyping of your Python models and ideas

Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more

Harness the power of TensorFlow by exporting saved models into web applications

Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content

Create dashboards with paywalls to offer subscription-based access

Access API data such as Google Maps, OpenWeather, etc.

Apply different approaches to make sense of text data and return customized intelligence

Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back

Utilize the freemium offerings of Google Analytics and analyze the results

Take your ideas all the way to your customer's plate using the top serverless cloud providers

Who This Book Is For

Those with some programming experience with Python, code editing, and access to an interpreter in working order. 

The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.

About the Author

Manuel Amunategui has decades of professional experience in programming, data science, and creating end-to-end solutions for customers in various industries. He sees informational and educational gaps in the industry. 

He has been fortunate to work with software at Microsoft, in finance on Wall Street, in research at one of the largest health systems in the US, and now as VP of Data Science at SpringML, a Google Cloud and Salesforce preferred partner. He understands what it takes to start new careers and new businesses.

Since 2013, he has been advocating for data science through blogs, vlogs, and educational material. He has grown and curated various highly focused and niche social media channels, including a YouTube channel with 60 videos and 350k views and a very popular applied data science blog. 

His teaching perspective is about welcoming any new comer with a desire to learn, creating material to quickly overcome learning curves, and demonstrating through clear narrative and practical examples that it is never as hard as most people think.

Mehdi Roopaei, PhD, is a postdoctoral fellow at Open Cloud Institute of University of Texas at San Antonio, with a research focus on data-driven decision-making systems. 

He has 12 years of experience in teaching at the university level, more than 980 citations for peer-reviewed publications, and two published books. His focus is on cloud machine learning, data analytics, and the AI-Thinking platform (proposed at HICSS51).


Chapter 1: Introduction to Serverless Technologies

Chapter 2: Client-Side Intelligence Using Regression Coefficients on Azure 

Chapter 3: Real-Time Intelligence with Logistic Regression on GCP

Chapter 4: Pretrained Intelligence with Gradient Boosting Machine on AWS

Chapter 5: Case Study Part 1: Supporting Both Web and Mobile Browsers 

Chapter 6: Displaying Predictions with Google Maps on Azure 195 Planning our Web Application 

Chapter 7: Forecasting with Naive Bayes and OpenWeather on AWS

Chapter 8: Interactive Drawing Canvas and Digit Predictions Using TensorFlow on GCP 

Chapter 9: Case Study Part 2: Displaying Dynamic Charts

Chapter 10: Recommending with Singular Value Decomposition on GCP 305 Planning Our Web Application

Chapter 11: Simplifying Complex Concepts with NLP and Visualization on Azure

Chapter 12: Case Study Part 3: Enriching Content with Fundamental Financial Information

Chapter 13: Google Analytics 

Chapter 14: A/B Testing on  PythonAnywhere and MySQL

Chapter 15: From Visitor to Subscriber

Chapter 16: Case Study Part 4: Building a Subscription Paywall with Memberful 

Chapter 17: Conclusion

About The Book:

Publisher ‏ : ‎ Apress; 1st ed. edition (September 13, 2018)

Language ‏ : ‎ English

Paperback ‏ : ‎ 523 pages

File: PDF, 9MB


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