Monetizing Machine Learning.

Picture Of The Book:

Free Book Monetizing Machine Learning pdf.
Monetizing Machine Learning



About The Book:



You will work through a series of common Python problems in data science in growing order of complexity. The practical projects presented in this book are simple and clear and can be used as templates to start many other project types. You will learn how to create a web app about numerical or categorical predictions, understand text analysis, create powerful and interactive presentations, restrict data access service, and take advantage of web plug-ins to accept credit card payments and donations. You will get your projects in the hands of the world in no time.

Information About The Book:



Title: Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud.
Language: English.
Size: 23.0 Mb.
Pages: 510.
Format: Pdf.
Year: 2018.
Edition: 1.
Author: Manuel Amunategui.

Contents Of The Book:



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.
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.
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.


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