Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and
International Administration, co-author of Monetizing Machine Learning and VP of Data Science at SpringML.
From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say:
It just ain’t real 'til it reaches your customer’s plate
I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied machine learning.
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.
This book aims to bridge the gap between data science and applied data science.
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 provider
Over a Dozen Practical Projects, Here Are They Highlights
Bike Rental Demand using Regression Coefficients and Microsoft Azure
Surviving the Titanic Shipwreck - Experiment with Passenger Profiles on Google Cloud
Can You Design a Top-Rated Wine? Predicting Wine Quality on Amazon Web Services
Wall Street & Pair Trading on PythonAnywhere For Web & Mobile
Where Will Crime Happen Next in San Francisco? Build a Predictive Mapping Dashboard using Google Maps and Microsoft Azure
Whether or not You Will Golf Tomorrow Using Real-Time Weather Prognostics on Amazon Web Services
Guess My Number with TensorFlow. Let's Build a Dashboard to Enable Visitors to Draw and Predict Digits using TensorFlow Image Classification on Google Cloud
Displaying Dynamic Stock Charts on PythonAnywhere
What to Watch Next? Recommending Movies on Google Cloud
Ham vs Spam and the Cost of Eliminating all Spam Messages on Microsoft Azure. Easily Explain Complex Topics with Interactive Web
A Fundamental Financial Information Service. Trading on PythonAnywhere
